Tootfinder

DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research
Xinyang Shao, Tri Kurniawan Wijaya
https://arxiv.org/abs/2508.10238 https://…

DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research
Accessing suitable datasets is critical for research and development in recommender systems. However, finding datasets that match specific recommendation task or domains remains a challenge due to scattered sources and inconsistent metadata. To address this gap, we propose a community-driven and explainable dataset search engine tailored for recommender system research. Our system supports semantic search across multiple dataset attributes, such as dataset names, descriptions, and recommendatio…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
Wencai Ye, Mingjie Sun, Shaoyun Shi, Peng Wang, Wenjin Wu, Peng Jiang
https://arxiv.org/abs/2508.10584 htt…

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
Semantic IDs are discrete identifiers generated by quantizing the Multi-modal Large Language Models (MLLMs) embeddings, enabling efficient multi-modal content integration in recommendation systems. However, their lack of collaborative signals results in a misalignment with downstream discriminative and generative recommendation objectives. Recent studies have introduced various alignment mechanisms to address this problem, but their two-stage framework design still leads to two main limitations…

Interesting: I was unsure if I should buy a 2nd hand OLED computer monitor for a specific price and if I really should because it's some kind of luxury for a few hundred Euros.
So I asked ChatGPT for (1) estimating a fair price given all the relevant information about that and then (2) describing my situation and letting it generate a /- list (to check if I have overseen an aspect) and a recommendation for buy/not buy.
The result was fascinating to me. One of the better use-…

"Research on the construction and application of problem-method-oriented academic graph empowered by LLM" https://doi.org/10.1007/s10791-025-09675-2
"Nowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers’ lit…

Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders
Ruixuan Sun, Junyuan Wang, Sanjali Roy, Joseph A. Konstan
https://arxiv.org/abs/2510.08930 ht…

Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders
Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized interest summaries of a user's movie history. Unlike static profiles, this design invites users to directly inspect, modify, and reflect on th…

MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest
Xiao Yang, Peifeng Yin, Abe Engle, Jinfeng Zhuang, Ling Leng
https://arxiv.org/abs/2510.09857

MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest
The lightweight ad ranking layer, living after the retrieval stage and before the fine ranker, plays a critical role in the success of a cascaded ad recommendation system. Due to the fact that there are multiple optimization tasks depending on the ad domain, e.g., Click Through Rate (CTR) for click ads and Conversion Rate (CVR) for conversion ads, as well as multiple surfaces where an ad is served (home feed, search, or related item recommendation) with diverse ad products (shopping or standard…

@…
A recommendation from my side:
A full stack #AI launchkit (docker based) for selfhosting with many apps including supabase, ollama, n8n, flowise, etc.

Go Fuck Yourself
but as a self-care recommendation

My #Series Recommendation 📺
👉 STICK ⛳ 🏌️
if you love Owen Wilson, you will love this funny "feel good" series ❤️

https://www.imdb.com/title/tt31710249/

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency
Xianwen Guo, Bin Huang, Xiaomeng Wu, Guanlin Wu, Fangjian Li, Shijia Wang, Qiang Xiao, Chuanjiang Luo, Yong Li
https://arxiv.org/abs/2509.22681

FLAME: A Serving System Optimized for Large-Scale Generative Recommendation with Efficiency
Generative recommendation (GR) models possess greater scaling power compared to traditional deep learning recommendation models (DLRMs), yet they also impose a tremendous increase in computational burden. Measured in FLOPs, a typical GR model's workload sits in $10^9 \sim 10^{11}$ range, roughly four orders of magnitude higher than traditional DLRMs. Delivering accurate results in a few tens of milliseconds while processing billions of such requests per day puts extreme demands on the performan…

What are some of your favorite YouTube channels that are not related to gaming, particularly those that are smaller or less well known? I am looking to expand my knowledge and diversify my FreeTube feed.
#Fediverse #YouTube

Sources: Meta is considering using Google's Gemini and open-source Gemma AI models to improve its ad summarization and recommendation system (Erin Woo/The Information)
https://www.theinformation.com/articles/meta-talks-google-use-gemini-improve-a…

Meta in Talks With Google to Use Gemini to Improve Ad Targeting
Meta Platforms staffers have had discussions with Google Cloud about the possibility of using Google’s artificial intelligence models to improve Meta’s ad business, according to people with knowledge of the conversations. The discussions are the latest sign of how Meta’s stumbles with its own AI ...

Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
Julia Ann Mathew, Suining He
https://arxiv.org/abs/2508.05652 …

Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
The increasing popularity of outdoor recreational activities (such as hiking and biking) has boosted the demand for a conversational AI system to provide informative and personalized suggestion on outdoor trails. Challenges arise in response to (1) how to provide accurate outdoor trail information via conversational AI; and (2) how to enable usable and efficient recommendation services. To address above, this paper discusses the preliminary and practical lessons learned from developing Judy, an…

Keine Gentechnik ohne Kennzeichnung in useren Einkaufläden.
https://weact.campact.de/petitions/neue-gentechnik-wahlfreiheit-sichern-ri…

The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning
Chunliang Hua, Xiao Hu, Jiayang Sun, Zeyuan Yang
https://arxiv.org/abs/2508.12651 https…

The Maximum Coverage Model and Recommendation System for UAV Vertiports Location Planning
As urban aerial mobility (UAM) infrastructure development accelerates globally, cities like Shenzhen are planning large-scale vertiport networks (e.g., 1,200+ facilities by 2026). Existing planning frameworks remain inadequate for this complexity due to historical limitations in data granularity and real-world applicability. This paper addresses these gaps by first proposing the Capacitated Dynamic Maximum Covering Location Problem (CDMCLP), a novel optimization framework that simultaneously mo…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

@… @… I second the Mint recommendation. Ubuntu is fine, but a bit confusing coming from Windows. Mint is an easier distro to figure out. But most of all, don't sweat it.

Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
Matou\v{s} Jel\'inek, Nadine Schlicker, Ewart de Visser
https://arxiv.org/abs/2508.02371 https://…

Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
Calibrated trust in automated systems (Lee and See 2004) is critical for their safe and seamless integration into society. Users should only rely on a system recommendation when it is actually correct and reject it when it is factually wrong. One requirement to achieve this goal is an accurate trustworthiness assessment, ensuring that the user's perception of the system's trustworthiness aligns with its actual trustworthiness, allowing users to make informed decisions about the extent to which …

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
Qian Yong, Yanhui Li, Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.04032

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
The feedback loop in industrial recommendation systems reinforces homogeneous content, creates filter bubble effects, and diminishes user satisfaction. Recently, large language models(LLMs) have demonstrated potential in serendipity recommendation, thanks to their extensive world knowledge and superior reasoning capabilities. However, these models still face challenges in ensuring the rationality of the reasoning process, the usefulness of the reasoning results, and meeting the latency requirem…

My recommendation for today:
Avoid the HP Instant Ink program as if it were the plague.
I have one printer on that program. They sent me a bad cartridge. I had to go through their awful "AI" system several times which merely told me to run test after test after test then clean the gold contacts than rub my belly while patting my head and reciting the Gettysburg Address... all of which ending up in them saying "we will send you a new cartridge in a couple of weeks…

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15233

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Recommendation systems (RS) personalize content by analyzing user preferences, but typically require centralized collection of user data, raising privacy and scalability concerns. Federated Recommendation Systems (FRS) address these issues by enabling distributed, privacy-preserving model training across edge devices, keeping raw data on-device. Although existing FRS frameworks benefit from on-device feature extraction and privacy preservation, they suffer from heterogeneous device capabilities…

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System
Hao Chen, Wenming Ma, Zihao Chu, Mingqi Li
https://arxiv.org/abs/2509.05115 https://

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System
In recent years, methods that combine contrastive learning with graph neural networks have emerged to address the challenges of recommendation systems, demonstrating powerful performance and playing a significant role in this domain. Contrastive learning primarily tackles the issue of data sparsity by employing data augmentation strategies, effectively alleviating this problem and showing promising results. Although existing research has achieved favorable outcomes, most current graph contrasti…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
Xiaoxi Cui, Weihai Lu, Yu Tong, Yiheng Li, Zhejun Zhao
https://arxiv.org/abs/2508.05352

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences…

An Accelerated Newton-GMRES Method for Multilinear PageRank
Maryam Boubekraoui, Ridwane Tahiri
https://arxiv.org/abs/2509.23374 https://arxiv.org/pdf/2509.…

An Accelerated Newton-GMRES Method for Multilinear PageRank
Modeling complex multiway relationships in large-scale networks is becoming more and more challenging in data science. The multilinear PageRank problem, arising naturally in the study of higher-order Markov chains, is a powerful framework for capturing such interactions, with applications in web ranking, recommendation systems, and social network analysis. It extends the classical Google PageRank model to a tensor-based formulation, leading to a nonlinear system that captures multi-way dependen…

From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system
Maxime Manderlier, Fabian Lecron, Olivier Vu Thanh, Nicolas Gillis
https://arxiv.org/abs/2509.18980

Strategic Pricing and Ranking in Recommendation Systems with Seller Competition
Tushar Shankar Walunj, Veeraruna Kavitha, Jayakrishnan Nair, Priyank Agarwal
https://arxiv.org/abs/2509.13462

Strategic Pricing and Ranking in Recommendation Systems with Seller Competition
We study a recommendation system where sellers compete for visibility by strategically offering commissions to a platform that optimally curates a ranked menu of items and their respective prices for each customer. Customers interact sequentially with the menu following a cascade click model, and their purchase decisions are influenced by price sensitivity and positions of various items in the menu. We model the seller-platform interaction as a Stackelberg game with sellers as leaders and consi…

MAAdvisor: Zero-Shot Index Advisor using Multi-Agent LLMs
Zhaodonghui Li, Haitao Yuan, Jiachen Shi, Hao Zhang, Yu Rong, Gao Cong
https://arxiv.org/abs/2508.16044 https://…

MAAdvisor: Zero-Shot Index Advisor using Multi-Agent LLMs
Index recommendation is one of the most important problems in database management system (DBMS) optimization. Given queries and certain index-related constraints, traditional methods rely on heuristic optimization or learning-based models to select effective indexes and improve query performance. However, heuristic optimization suffers from high computation time, and learning-based models lose generalisability due to training for different workloads and database schemas. With the recent rapid d…

A Metric for MLLM Alignment in Large-scale Recommendation
Yubin Zhang, Yanhua Huang, Haiming Xu, Mingliang Qi, Chang Wang, Jiarui Jin, Xiangyuan Ren, Xiaodan Wang, Ruiwen Xu
https://arxiv.org/abs/2508.04963

A Metric for MLLM Alignment in Large-scale Recommendation
Multimodal recommendation has emerged as a critical technique in modern recommender systems, leveraging content representations from advanced multimodal large language models (MLLMs). To ensure these representations are well-adapted, alignment with the recommender system is essential. However, evaluating the alignment of MLLMs for recommendation presents significant challenges due to three key issues: (1) static benchmarks are inaccurate because of the dynamism in real-world applications, (2) e…

TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling
Seungheon Doh, Keunwoo Choi, Juhan Nam
https://arxiv.org/abs/2510.01698 https://a…

TalkPlay-Tools: Conversational Music Recommendation with LLM Tool Calling
While the recent developments in large language models (LLMs) have successfully enabled generative recommenders with natural language interactions, their recommendation behavior is limited, leaving other simpler yet crucial components such as metadata or attribute filtering underutilized in the system. We propose an LLM-based music recommendation system with tool calling to serve as a unified retrieval-reranking pipeline. Our system positions an LLM as an end-to-end recommendation system that i…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation
Yicong Wu, Ting Chen, Irit Hochberg, Zhoujian Sun, Ruth Edry, Zhengxing Huang, Mor Peleg
https://arxiv.org/abs/2507.10911

Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation
Therapy recommendation for chronic patients with multimorbidity is challenging due to risks of treatment conflicts. Existing decision support systems face scalability limitations. Inspired by the way in which general practitioners (GP) manage multimorbidity patients, occasionally convening multidisciplinary team (MDT) collaboration, this study investigated the feasibility and value of using a Large Language Model (LLM)-based multi-agent system (MAS) for safer therapy recommendations. We designe…

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
Aleksandra Osowska-Kurczab, Klaudia Nazarko, Mateusz Marzec, Lidia Wojciechowska, Eli\v{s}ka Kreme\v{n}ov\'a
https://arxiv.org/abs/2508.03702

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
Building large-scale e-commerce recommendation systems requires addressing three key technical challenges: (1) designing a universal recommendation architecture across dozens of placements, (2) decreasing excessive maintenance costs, and (3) managing a highly dynamic product catalogue. This paper presents a unified content-based recommendation system deployed at Allegro.com, the largest e-commerce platform of European origin. The system is built on a prevalent Two Tower retrieval framework, rep…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

FIRE: Faithful Interpretable Recommendation Explanations
S. M. F. Sani, Asal Meskin, Mohammad Amanlou, Hamid R. Rabiee
https://arxiv.org/abs/2508.05225 https://

FIRE: Faithful Interpretable Recommendation Explanations
Natural language explanations in recommender systems are often framed as a review generation task, leveraging user reviews as ground-truth supervision. While convenient, this approach conflates a user's opinion with the system's reasoning, leading to explanations that may be fluent but fail to reflect the true logic behind recommendations. In this work, we revisit the core objective of explainable recommendation: to transparently communicate why an item is recommended by linking user needs to r…

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search
Sukjin Kim, Seongyeon Park, Si Ung Noh, Junguk Hong, Taehee Kwon, Hunseong Lim, Jinho Lee
https://arxiv.org/abs/2507.17094

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search
Graph-based Approximate Nearest Neighbor Search (ANNS) is widely adopted in numerous applications, such as recommendation systems, natural language processing, and computer vision. While recent works on GPU-based acceleration have significantly advanced ANNS performance, the ever-growing scale of datasets now demands efficient multi-GPU solutions. However, the design of existing works overlooks multi-GPU scalability, resulting in naive approaches that treat additional GPUs as a means to extend …

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li, Xiaoxiao Xu, Jiaqi Yu, Zixiu Wang, Lantao Hu, Han Li, Kun Gai
https://arxiv.org/abs/2508.05093

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-tru…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Audio Prototypical Network For Controllable Music Recommendation
F{\i}rat \"Oncel, Emiliano Penaloza, Haolun Wu, Shubham Gupta, Mirco Ravanelli, Laurent Charlin, Cem Subakan
https://arxiv.org/abs/2508.00194

Audio Prototypical Network For Controllable Music Recommendation
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While these models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system's modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, t…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/8]:
- Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System
Lei Zheng, Ning Li, Weinan Zhang, Yong Yu

UniSearch: Rethinking Search System with a Unified Generative Architecture
Jiahui Chen, Xiaoze Jiang, Zhibo Wang, Quanzhi Zhu, Junyao Zhao, Feng Hu, Kang Pan, Ao Xie, Maohua Pei, Zhiheng Qin, Hongjing Zhang, Zhixin Zhai, Xiaobo Guo, Runbin Zhou, Kefeng Wang, Mingyang Geng, Cheng Chen, Jingshan Lv, Yupeng Huang, Xiao Liang, Han Li
https://a…

UniSearch: Rethinking Search System with a Unified Generative Architecture
Modern search systems play a crucial role in facilitating information acquisition. Traditional search engines typically rely on a cascaded architecture, where results are retrieved through recall, pre-ranking, and ranking stages. The complexity of designing and maintaining multiple modules makes it difficult to achieve holistic performance gains. Recent advances in generative recommendation have motivated the exploration of unified generative search as an alternative. However, existing approach…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms
AKM Bahalul Haque, A. K. M. Najmul Islam, Patrick Mikalef
https://arxiv.org/abs/2508.16610

To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms
AI based social media recommendations have great potential to improve the user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation system being a black box creates comprehensibility and transparency issues. This paper investigates social media recommendations from an end user perspective. For the investigation, we used the popular social media platform Facebook and recruited regular users…

Personalized Recommendation of Dish and Restaurant Collections on iFood
Fernando F. Granado, Davi A. Bezerra, Iuri Queiroz, Nathan Oliveira, Pedro Fernandes, Bruno Schock
https://arxiv.org/abs/2508.03670

Personalized Recommendation of Dish and Restaurant Collections on iFood
Food delivery platforms face the challenge of helping users navigate vast catalogs of restaurants and dishes to find meals they truly enjoy. This paper presents RED, an automated recommendation system designed for iFood, Latin America's largest on-demand food delivery platform, to personalize the selection of curated food collections displayed to millions of users. Our approach employs a LightGBM classifier that scores collections based on three feature groups: collection characteristics, user-…

Algorithm Adaptation Bias in Recommendation System Online Experiments
Chen Zheng, Zhenyu Zhao
https://arxiv.org/abs/2509.00199 https://arxiv.org/pdf/2509.0…

Algorithm Adaptation Bias in Recommendation System Online Experiments
Online experiments (A/B tests) are widely regarded as the gold standard for evaluating recommender system variants and guiding launch decisions. However, a variety of biases can distort the results of the experiment and mislead decision-making. An underexplored but critical bias is algorithm adaptation effect. This bias arises from the flywheel dynamics among production models, user data, and training pipelines: new models are evaluated on user data whose distributions are shaped by the incumbe…

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
Boyuan Long, Yueqi Wang, Hiloni Mehta, Mick Zomnir, Omkar Pathak, Changping Meng, Ruolin Jia, Yajun Peng, Dapeng Hong, Xia Wu, Mingyan Gao, Onkar Dalal, Ningren Han
https://arxiv.org/abs/2510.06657

LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations
This paper presents a case study on deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within a large-scale industrial short-form video recommendation system. Traditional machine learning classifiers for content understanding face protracted development cycles and a lack of deep, nuanced comprehension. The "LLM-as-annotators" approach addresses these by significantly shortening developm…

CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods
Gustavo H Santos, Myriam Delgado, Thiago H Silva
https://arxiv.org/abs/2507.18778 https://…

CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods
We present CityHood, an interactive and explainable recommendation system that suggests cities and neighborhoods based on users' areas of interest. The system models user interests leveraging large-scale Google Places reviews enriched with geographic, socio-demographic, political, and cultural indicators. It provides personalized recommendations at city (Core-Based Statistical Areas - CBSAs) and neighborhood (ZIP code) levels, supported by an explainable technique (LIME) and natural-language ex…

Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training
Yi-Ping Hsu, Po-Wei Wang, Chantat Eksombatchai, Jiajing Xu
https://arxiv.org/abs/2508.18700

Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training
ID-based embeddings are widely used in web-scale online recommendation systems. However, their susceptibility to overfitting, particularly due to the long-tail nature of data distributions, often limits training to a single epoch, a phenomenon known as the "one-epoch problem." This challenge has driven research efforts to optimize performance within the first epoch by enhancing convergence speed or feature sparsity. In this study, we introduce a novel two-stage training strategy that incorporat…

Federated Consistency- and Complementarity-aware Consensus-enhanced Recommendation
Yunqi Mi, Boyang Yan, Guoshuai Zhao, Jialie Shen, Xueming Qian
https://arxiv.org/abs/2509.22659

Federated Consistency- and Complementarity-aware Consensus-enhanced Recommendation
Personalized federated recommendation system (FedRec) has gained significant attention for its ability to preserve privacy in delivering tailored recommendations. To alleviate the statistical heterogeneity challenges among clients and improve personalization, decoupling item embeddings into the server and client-specific views has become a promising way. Among them, the global item embedding table serves as a consensus representation that integrates and reflects the collective patterns across a…

AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
Qixin Wang, Dawei Wang, Kun Chen, Yaowei Hu, Puneet Girdhar, Ruoteng Wang, Aadesh Gupta, Chaitanya Devella, Wenlai Guo, Shangwen Huang, Bachir Aoun, Greg Hayworth, Han Li, Xintao Wu
https://arxiv.org/abs/2508.13423

AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
In recent years, recommendation systems have evolved from providing a single list of recommendations to offering a comprehensive suite of topic focused services. To better accomplish this task, conversational recommendation systems (CRS) have progressed from basic retrieval augmented LLM generation to agentic systems with advanced reasoning and self correction capabilities. However, agentic systems come with notable response latency, a longstanding challenge for conversational recommendation sy…

Proposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results
Ali Fallahi, Azam Bastanfard, Amineh Amini, Hadi Saboohi
https://arxiv.org/abs/2507.21770 …

Proposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results
The importance of recommender systems on the web has grown, especially in the movie industry, with a vast selection of options to watch. To assist users in traversing available items and finding relevant results, recommender systems analyze operational data and investigate users' tastes and habits. Providing highly individualized suggestions can boost user engagement and satisfaction, which is one of the fundamental goals of the movie industry, significantly in online platforms. According to re…

RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou, Zhihua Han, Zhensong Yan, Mingjie Liu, Hechen Sun, Jiawei Liu, Yiwen Hu, Qi Wang, YiHan Xian, Wenjie Guo, Houyuan Xiang, Zhiyuan Zeng, Xiangrong Sheng, Bencheng Yan, Nan Hu, Yuheng Huang, Jinqing Lian, Ziru Xu, Yan Zhang, Ju Huang, Siran Yang, Huimin Yi, Jiamang Wang, Pengjie Wang, Han Zhu, Jian Wu, Dan Ou, Jian Xu, Haihong Tang, Yuning Jiang, Bo Zheng, Lin Qu

RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training framework based on the PyTorch ecosystem that meets the training needs of industrial-grade recommendation models that integrated with large models. 2.System Optimization To optimize the sparse component, offering superior efficiency over the TensorFlow-based recommendation models. The dense component, meanwhile, leverage…

Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
Yubo Wang, Min Tang, Nuo Shen, Shujie Cui, Weiqing Wang
https://arxiv.org/abs/2508.03703 http…

Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
The large language model (LLM) powered recommendation paradigm has been proposed to address the limitations of traditional recommender systems, which often struggle to handle cold start users or items with new IDs. Despite its effectiveness, this study uncovers that LLM empowered recommender systems are vulnerable to reconstruction attacks that can expose both system and user privacy. To examine this threat, we present the first systematic study on inversion attacks targeting LLM empowered reco…

Understanding Distribution Structure on Calibrated Recommendation Systems
Diego Correa da Silva, Denis Robson Dantas Boaventura, Mayki dos Santos Oliveira, Eduardo Ferreira da Silva, Joel Machado Pires, Frederico Ara\'ujo Dur\~ao
https://arxiv.org/abs/2508.13568

Understanding Distribution Structure on Calibrated Recommendation Systems
Traditional recommender systems aim to generate a recommendation list comprising the most relevant or similar items to the user's profile. These approaches can create recommendation lists that omit item genres from the less prominent areas of a user's profile, thereby undermining the user's experience. To solve this problem, the calibrated recommendation system provides a guarantee of including less representative areas in the recommended list. The calibrated context works with three distributi…

The Hidden Cost of Defaults in Recommender System Evaluation
Hannah Berlin, Robin Svahn, Alan Said
https://arxiv.org/abs/2508.21180 https://arxiv.org/pdf/2…

The Hidden Cost of Defaults in Recommender System Evaluation
Hyperparameter optimization is critical for improving the performance of recommender systems, yet its implementation is often treated as a neutral or secondary concern. In this work, we shift focus from model benchmarking to auditing the behavior of RecBole, a widely used recommendation framework. We show that RecBole's internal defaults, particularly an undocumented early-stopping policy, can prematurely terminate Random Search and Bayesian Optimization. This limits search coverage in ways tha…

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
Ronald Carvalho Boadana, Ademir Guimar\~aes da Costa Junior, Ricardo Rios, F\'abio Santos da Silva
https://arxiv.org/abs/2508.11671

LLM-Based Intelligent Agents for Music Recommendation: A Comparison with Classical Content-Based Filtering
The growing availability of music on streaming platforms has led to information overload for users. To address this issue and enhance the user experience, increasingly sophisticated recommendation systems have been proposed. This work investigates the use of Large Language Models (LLMs) from the Gemini and LLaMA families, combined with intelligent agents, in a multi-agent personalized music recommendation system. The results are compared with a traditional content-based recommendation model, co…

Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items
Maria Vlachou
https://arxiv.org/abs/2507.18017 http…

Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items
In Conversational Recommendation Systems (CRS), a user provides feedback on recommended items at each turn, leading the CRS towards improved recommendations. Due to the need for a large amount of data, a user simulator is employed for both training and evaluation. Such user simulators critique the current retrieved item based on knowledge of a single target item. However, system evaluation in offline settings with simulators is limited by the focus on a single target item and their unlimited pa…

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
Hao Gu, Rui Zhong, Yu Xia, Wei Yang, Chi Lu, Peng Jiang, Kun Gai
https://arxiv.org/abs/2507.17249

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily involve basic prompt techniques for knowledge acquisition, which resemble System-1 thinking. This makes these methods highly sensitive to errors in the reasoning path, where even a small mistake can lead to an incorrect inference. To this end, in this paper, we propose $R^{4}$ec, a reasoning, reflection and refinement…

What News Recommendation Research Did (But Mostly Didn't) Teach Us About Building A News Recommender
Karl Higley, Robin Burke, Michael D. Ekstrand, Bart P. Knijnenburg
https://arxiv.org/abs/2509.12361

What News Recommendation Research Did (But Mostly Didn't) Teach Us About Building A News Recommender
One of the goals of recommender systems research is to provide insights and methods that can be used by practitioners to build real-world systems that deliver high-quality recommendations to actual people grounded in their genuine interests and needs. We report on our experience trying to apply the news recommendation literature to build POPROX, a live platform for news recommendation research, and reflect on the extent to which the current state of research supports system-building efforts. Ou…

FedFlex: Federated Learning for Diverse Netflix Recommendations
Sven Lankester, Manel Slokom, Gustavo de Carvalho Bertoli, Matias Vizcaino, Emmanuelle Beauxis Aussalet, Laura Hollink
https://arxiv.org/abs/2507.21115

FedFlex: Federated Learning for Diverse Netflix Recommendations
Federated learning is a decentralized approach that enables collaborative model training across multiple devices while preserving data privacy. It has shown significant potential in various domains, including healthcare and personalized recommendation systems. However, most existing work on federated recommendation systems has focused primarily on improving accuracy, with limited attention to fairness and diversity. In this paper, we introduce FedFlex, a federated recommender system for Netflix…

Interactive Recommendation Agent with Active User Commands
Jiakai Tang, Yujie Luo, Xunke Xi, Fei Sun, Xueyang Feng, Sunhao Dai, Chao Yi, Dian Chen, Zhujin Gao, Yang Li, Xu Chen, Wen Chen, Jian Wu, Yuning Jiang, Bo Zheng
https://arxiv.org/abs/2509.21317

Interactive Recommendation Agent with Active User Commands
Traditional recommender systems rely on passive feedback mechanisms that limit users to simple choices such as like and dislike. However, these coarse-grained signals fail to capture users' nuanced behavior motivations and intentions. In turn, current systems cannot also distinguish which specific item attributes drive user satisfaction or dissatisfaction, resulting in inaccurate preference modeling. These fundamental limitations create a persistent gap between user intentions and system interp…

Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation
Shouxing Ma, Yawen Zeng, Shiqing Wu, Guandong Xu
https://arxiv.org/abs/2508.13745 https://

Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation
Multi-modal recommender system focuses on utilizing rich modal information ( i.e., images and textual descriptions) of items to improve recommendation performance. The current methods have achieved remarkable success with the powerful structure modeling capability of graph neural networks. However, these methods are often hindered by sparse data in real-world scenarios. Although contrastive learning and homography ( i.e., homogeneous graphs) are employed to address the data sparsity challenge, …

Research on Conversational Recommender System Considering Consumer Types
Yaying Luo, Hui Fang, Zhu Sun
https://arxiv.org/abs/2508.13209 https://arxiv.org/p…

Research on Conversational Recommender System Considering Consumer Types
Conversational Recommender Systems (CRS) provide personalized services through multi-turn interactions, yet most existing methods overlook users' heterogeneous decision-making styles and knowledge levels, which constrains both accuracy and efficiency. To address this gap, we propose CT-CRS (Consumer Type-Enhanced Conversational Recommender System), a framework that integrates consumer type modeling into dialogue recommendation. Based on consumer type theory, we define four user categories--depe…

OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Sunhao Dai, Jiakai Tang, Jiahua Wu, Kun Wang, Yuxuan Zhu, Bingjun Chen, Bangyang Hong, Yu Zhao, Cong Fu, Kangle Wu, Yabo Ni, Anxiang Zeng, Wenjie Wang, Xu Chen, Jun Xu, See-Kiong Ng
https://arxiv.org/abs/2509.18091

OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, w…

Collaborative Filtering using Variational Quantum Hopfield Associative Memory
Amir Kermanshahani, Ebrahim Ardeshir-Larijani, Rakesh Saini, Saif Al-Kuwari
https://arxiv.org/abs/2508.14906

Collaborative Filtering using Variational Quantum Hopfield Associative Memory
Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machine Learning (QML), which integrates quantum computing with machine learning techniques, presents powerful new tools for data processing and pattern recognition. This paper proposes a hybrid recommendation system that combines Quantum Hopfield Associative Memory (QHAM) with deep n…

Bootstrapping Conditional Retrieval for User-to-Item Recommendations
Hongtao Lin, Haoyu Chen, Jaewon Jang, Jiajing Xu
https://arxiv.org/abs/2508.16793 https://

Bootstrapping Conditional Retrieval for User-to-Item Recommendations
User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency. In this work, we focus on a variant called \textit{conditional retrieval}, where we expect retrieved items to be relevant to a condition (e.g. topic). We propose a method that uses the same training data as standard two tower models but incorporates item-side information as conditions in query. This allows us to bootstrap new c…

RecPS: Privacy Risk Scoring for Recommender Systems
Jiajie He, Yuechun Gu, Keke Chen
https://arxiv.org/abs/2507.18365 https://arxiv.org/pdf/2507.18365

RecPS: Privacy Risk Scoring for Recommender Systems
Recommender systems (RecSys) have become an essential component of many web applications. The core of the system is a recommendation model trained on highly sensitive user-item interaction data. While privacy-enhancing techniques are actively studied in the research community, the real-world model development still depends on minimal privacy protection, e.g., via controlled access. Users of such systems should have the right to choose \emph{not} to share highly sensitive interactions. However, …