Search Results for author: Allen Lin

Found 8 papers, 2 papers with code

Federated Conversational Recommender System

no code implementations2 Mar 2025 Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee

To address the user privacy concerns in CRS, we first define a set of privacy protection guidelines for preserving user privacy under the conversational recommendation setting.

Conversational Recommendation Recommendation Systems

Towards An Efficient LLM Training Paradigm for CTR Prediction

no code implementations2 Mar 2025 Allen Lin, Renqin Cai, Yun He, Hanchao Yu, Jing Qian, Rui Li, Qifan Wang, James Caverlee

Despite such promising results, the computational inefficiency inherent in the current training paradigm makes it particularly challenging to train LLMs for ranking-based recommendation tasks on large datasets.

Click-Through Rate Prediction Prediction

Countering Mainstream Bias via End-to-End Adaptive Local Learning

1 code implementation13 Apr 2024 Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin, James Caverlee

In this paper, we identify two root causes of this mainstream bias: (i) discrepancy modeling, whereby CF algorithms focus on modeling mainstream users while neglecting niche users with unique preferences; and (ii) unsynchronized learning, where niche users require more training epochs than mainstream users to reach peak performance.

Collaborative Filtering Mixture-of-Experts

Enhancing User Personalization in Conversational Recommenders

no code implementations13 Feb 2023 Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee

Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience.

Attribute Conversational Recommendation

Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems

no code implementations5 Aug 2022 Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee

Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation.

Attribute Recommendation Systems

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