Tootfinder

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

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

Building a Recommendation System Using Amazon Product Co-Purchasing Network
Minghao Liu, Catherine Zhao, Nathan Zhou
https://arxiv.org/abs/2506.02482 https…

Building a Recommendation System Using Amazon Product Co-Purchasing Network
This project develops an online, inductive recommendation system for newly listed products on e-commerce platforms, focusing on suggesting relevant new items to customers as they purchase other products. Using the Amazon Product Co-Purchasing Network Metadata dataset, we construct a co-purchasing graph where nodes represent products and edges capture co-purchasing relationships. To address the challenge of recommending new products with limited information, we apply a modified GraphSAGE method …

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang, Yan Feng, Zhe Wang, Xiaowei Shi, An You, Yu Chen
https://arxiv.org/abs/2506.00828

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only pointwise modeling solutions can be adopted, focusing solely on modeling the likelihood of clicks or conversions for items by users to learn user-item preferences, without the ability to capture the ranking information among different items directly. However, since user-side information is often much more abundant tha…

Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q's
Ramesh Johari, Tianyi Peng, Wenqian Xing
https://arxiv.org/abs/2506.05308

Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q's
Randomized controlled experiments (''A/B testing'') are fundamental for assessing interventions in dynamic technology-driven environments, such as recommendation systems, online marketplaces, and digital health interventions. In these systems, interventions typically impact not only the current state of the system, but also future states; therefore, accurate estimation of the global average treatment effect (or GATE) from experiments requires accounting for the dynamic temporal behavior of the …

TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Xue Xia, Saurabh Vishwas Joshi, Kousik Rajesh, Kangnan Li, Yangyi Lu, Nikil Pancha, Dhruvil Deven Badani, Jiajing Xu, Pong Eksombatchai
https://arxiv.org/abs/2506.02267

TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industry-scale CTR models often rely on short user sequences, limiting their ability to capture long-term behavior. Additionally, these models typically lack an integrated action-prediction task within a point-wise ranking framework, reducing their predictive power. They also rarely address the infrastructure challenges invo…

UK 2025 #Publication
Severe Space Weather Impacts on UK Critical National Infrastructure
A Space weather instrumentation, measurement, modelling and risk (#SWIMMR) Project Report
Priority Recommendation CS6 : "That government and other policymakers ensure that s…

Quake: Adaptive Indexing for Vector Search
Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
https://arxiv.org/abs/2506.03437

Quake: Adaptive Indexing for Vector Search
Vector search, the task of finding the k-nearest neighbors of high-dimensional vectors, underpins many machine learning applications, including recommendation systems and information retrieval. However, existing approximate nearest neighbor (ANN) methods perform poorly under dynamic, skewed workloads where data distributions evolve. We introduce Quake, an adaptive indexing system that maintains low latency and high recall in such environments. Quake employs a hierarchical partitioning scheme th…

I saw a recommendation from Robin Hobb about the first book of the Murderbot series by @… yesterday and since I was between books I jumped right in.
Having read the first one I am excited to keep going. Funniest sci-fi I've read in years.
#SciFi

This https://arxiv.org/abs/2505.17549 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

EGA: A Unified End-to-End Generative Framework for Industrial Advertising Systems
Online industrial advertising system is fundamentally constrained by the inefficiency of multi-stage cascaded architectures, which filter out high-potential candidates early and fragment business decision logic across independent modules. Although recent advances in generative recommendation offer end-to-end solutions, they fall short of practical advertising requirements, lacking explicit modeling of bidding, creative selection, allocation mechanism, and payment computation that are essential …

A friend of mine, after watching « Lucifer » on my recommendation, shared this nugget of wisdom: the Lucifer character archetype is basically Ryō Saeba from « City Hunter ». I agree.
#anime #series

I'm going to the #Nextcloud Summit in June! 😊 My family will join me for a weekend in #Munich. Any recommendation for a family friendly #hotel (3 girls between 5 an 10 yo)?
#münchen #followerpower