Search Results for author: Ambarish Jash

Found 8 papers, 0 papers with code

REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives

no code implementations14 Mar 2025 Kun Su, Krishna Sayana, Hubert Pham, James Pine, Yuri Vasilevski, Raghavendra Vasudeva, Marialena Kyriakidi, Liam Hebert, Ambarish Jash, Anushya Subbiah, Sukhdeep Sodhi

Further, we establish an end-to-end modeling benchmark for the task of conversational recommendation, where models are trained to generate both recommendations and corresponding narratives conditioned on user history (items and critiques).

Conversational Recommendation

Beyond Retrieval: Generating Narratives in Conversational Recommender Systems

no code implementations22 Oct 2024 Krishna Sayana, Raghavendra Vasudeva, Yuri Vasilevski, Kun Su, Liam Hebert, James Pine, Hubert Pham, Ambarish Jash, Sukhdeep Sodhi

And to the best of our knowledge, represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives.

Conversational Recommendation Recommendation Systems +2

FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement

no code implementations18 Sep 2024 Liam Hebert, Marialena Kyriakidi, Hubert Pham, Krishna Sayana, James Pine, Sukhdeep Sodhi, Ambarish Jash

With revised baselines for item ID-only models, this paper also establishes new competitive results for architectures that combine IDs and textual descriptions.

Collaborative Filtering Language Modeling +2

PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting

no code implementations2 Aug 2024 Liam Hebert, Krishna Sayana, Ambarish Jash, Alexandros Karatzoglou, Sukhdeep Sodhi, Sumanth Doddapaneni, Yanli Cai, Dima Kuzmin

Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences.

User Embedding Model for Personalized Language Prompting

no code implementations10 Jan 2024 Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash, Sukhdeep Sodhi, Dima Kuzmin

Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recommendations.

model Recommendation Systems

Multi-Task End-to-End Training Improves Conversational Recommendation

no code implementations8 May 2023 Naveen Ram, Dima Kuzmin, Ellie Ka In Chio, Moustafa Farid Alzantot, Santiago Ontanon, Ambarish Jash, Judith Yue Li

In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue.

Conversational Recommendation Dialogue Management +2

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