Tootfinder

Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Maxim Zhelnin, Dmitry Redko, Volkov Daniil, Anna Volodkevich, Petr Sokerin, Valeriy Shevchenko, Egor Shvetsov, Alexey Vasilev, Darya Denisova, Ruslan Izmailov, Alexey Zaytsev
https://arxiv.org/abs/2509.09682…

Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-changing user preferences. However, training transformer-based SR models often encounters a high computational cost associated with scoring extensive item catalogs, often exceeding thousands of items. This occurs mainly due to the use of cross-entropy loss, where peak memory scales proportionally to catalog size, batch s…

Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs
Firas Jarboui, Issa Memari
https://arxiv.org/abs/2509.06185

Modeling shopper interest broadness with entropy-driven dialogue policy in the context of arbitrarily large product catalogs
Conversational recommender systems promise rich interactions for e-commerce, but balancing exploration (clarifying user needs) and exploitation (making recommendations) remains challenging, especially when deploying large language models (LLMs) with vast product catalogs. We address this challenge by modeling the breadth of user interest via the entropy of retrieval score distributions. Our method uses a neural retriever to fetch relevant items for a user query and computes the entropy of the r…

RoboEye: Enhancing 2D Robotic Object Identification with Selective 3D Geometric Keypoint Matching
Xingwu Zhang, Guanxuan Li, Zhuocheng Zhang, Zijun Long
https://arxiv.org/abs/2509.14966

RoboEye: Enhancing 2D Robotic Object Identification with Selective 3D Geometric Keypoint Matching
The rapidly growing number of product categories in large-scale e-commerce makes accurate object identification for automated packing in warehouses substantially more difficult. As the catalog grows, intra-class variability and a long tail of rare or visually similar items increase, and when combined with diverse packaging, cluttered containers, frequent occlusion, and large viewpoint changes-these factors amplify discrepancies between query and reference images, causing sharp performance drops…

AudioBoost: Increasing Audiobook Retrievability in Spotify Search with Synthetic Query Generation
Enrico Palumbo, Gustavo Penha, Alva Liu, Marcus Eltscheminov, Jefferson Carvalho dos Santos, Alice Wang, Hugues Bouchard, Humberto Jes\'us Corona Pampin, Michelle Tran Luu
https://arxiv.org/abs/2509.06452

AudioBoost: Increasing Audiobook Retrievability in Spotify Search with Synthetic Query Generation
Spotify has recently introduced audiobooks as part of its catalog, complementing its music and podcast offering. Search is often the first entry point for users to access new items, and an important goal for Spotify is to support users in the exploration of the audiobook catalog. More specifically, we would like to enable users without a specific item in mind to broadly search by topic, genre, story tropes, decade, and discover audiobooks, authors and publishers they may like. To do this, we ne…

AgentDR Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Mingdai Yang, Nurendra Choudhary, Jiangshu Du, Edward W. Huang, Philip S. Yu, Karthik Subbian, Danai Kourta
https://arxiv.org/abs/2510.05598

AgentDR Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-existent items and full-catalog ranking. Besides, a largely underexplored opportunity lies in leveraging LLMs'commonsense reasoning to capture user intent through substitute and complement relationships between items, which are usually implicit in datasets and difficult for traditional ID-based recommenders to capture. …