4.3k citations · 10.2k across the 68 of their papers we have counts for
20 papers · 1 filter
Density Weighting for Multi-Interest Personalized Recommendation
Nikhil Mehta, Anima Singh, Xinyang Yi +3
Using multiple user representations (MUR) to model user behavior instead of a single user representation (SUR) has been shown to improve personalization in recommendation systems.…
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
Jianling Wang, Haokai Lu, Sai zhang +10
Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh (and t…
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems
Pan Li, Yuyan Wang, Ed H. Chi +1
Recommending novel content, which expands user horizons by introducing them to new interests, has been shown to improve users' long-term experience on recommendation platforms \cit…
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
Pan Li, Yuyan Wang, Ed H. Chi +1
Existing aspect extraction methods mostly rely on explicit or ground truth aspect information, or using data mining or machine learning approaches to extract aspects from implicit…
Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
Anima Singh, Trung Vu, Nikhil Mehta +9
Randomly-hashed item ids are used ubiquitously in recommendation models. However, the learned representations from random hashing prevents generalization across similar items, caus…
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi +6
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can parti…