133 citations · 133 across the 7 of their papers we have counts for
6 papers · 1 filter
Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems
Yaochen Zhu, Harald Steck, James McInerney +4
Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences. In recommender systems, however, u…
Does Weighting Improve Matrix Factorization for Recommender Systems?
Alex Ayoub, Samuel Robertson, Dawen Liang +2
Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic…
Rank-GRPO: Training LLM-based Conversational Recommender Systems with Reinforcement Learning
Yaochen Zhu, Harald Steck, Dawen Liang +4
Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning…
Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems
Yaochen Zhu, Chao Wan, Harald Steck +4
Conversational recommender systems (CRS) aim to provide personalized recommendations via interactive dialogues with users. While large language models (LLMs) enhance CRS with their…
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
Zhankui He, Zhouhang Xie, Harald Steck +4
Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating…
Is Cosine-Similarity of Embeddings Really About Similarity?
Harald Steck, Chaitanya Ekanadham, Nathan Kallus
Cosine-similarity is the cosine of the angle between two vectors, or equivalently the dot product between their normalizations. A popular application is to quantify semantic simila…