Hubert Pham
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View article: Beyond Retrieval: Generating Narratives in Conversational Recommender Systems
Beyond Retrieval: Generating Narratives in Conversational Recommender Systems Open
The recent advances in Large Language Model's generation and reasoning capabilities present an opportunity to develop truly conversational recommendation systems. However, effectively integrating recommender system knowledge into LLMs for …
View article: FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement Open
Recent proposals in recommender systems represent items with their textual description, using a large language model. They show better results on standard benchmarks compared to an item ID-only model, such as Bert4Rec. In this work, we rev…
View article: Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies Open
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle …
View article: Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors
Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors Open
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e.g., clicks, item consumption, ratings). They allow users to express …
View article: RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems Open
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn increasing attention i…
View article: Folding
Folding Open
In recommender systems based on low-rank factorization of a partially observed user-item matrix, a common phenomenon that plagues many otherwise effective models is the interleaving of good and spurious recommendations in the top-K results…