Search Results for author: Minmin Chen

Found 27 papers, 3 papers with code

Large Language Models as Data Augmenters for Cold-Start Item Recommendation

no code implementations18 Feb 2024 Jianling Wang, Haokai Lu, James Caverlee, Ed Chi, Minmin Chen

The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems.

Recommendation Systems

Correction with Backtracking Reduces Hallucination in Summarization

no code implementations24 Oct 2023 Zhenzhen Liu, Chao Wan, Varsha Kishore, Jin Peng Zhou, Minmin Chen, Kilian Q. Weinberger

The results show that CoBa is effective and efficient in reducing hallucination, and offers great adaptability and flexibility.

Abstractive Text Summarization Hallucination

Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations

no code implementations2 Jun 2023 Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen

For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model.

Aspect Extraction

Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems

no code implementations2 Jun 2023 Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen

Hierarchical structure, on the other hand, exists in a user's novelty-seeking intent, which is manifested as a static and intrinsic user preference for seeking novelty along with a dynamic session-based propensity.

Hierarchical Reinforcement Learning Recommendation Systems +1

Large Language Models for User Interest Journeys

no code implementations24 May 2023 Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu, Yifat Amir, Samer Chucri, Pierce Vollucci, Fabio Soldo, Dina Bseiso, Sarah Scodel, Lucas Dixon, Ed H. Chi, Minmin Chen

We argue, and demonstrate through extensive experiments, that LLMs as foundation models can reason through user activities, and describe their interests in nuanced and interesting ways, similar to how a human would.

Natural Language Understanding Recommendation Systems

Long-Term Value of Exploration: Measurements, Findings and Algorithms

no code implementations12 May 2023 Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen

We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.

Recommendation Systems

Latent User Intent Modeling for Sequential Recommenders

no code implementations17 Nov 2022 Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen

We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.

Recommendation Systems

Reward Shaping for User Satisfaction in a REINFORCE Recommender

no code implementations30 Sep 2022 Konstantina Christakopoulou, Can Xu, Sai Zhang, Sriraj Badam, Trevor Potter, Daniel Li, Hao Wan, Xinyang Yi, Ya Le, Chris Berg, Eric Bencomo Dixon, Ed H. Chi, Minmin Chen

How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?

Imputation Reinforcement Learning (RL)

Recency Dropout for Recurrent Recommender Systems

no code implementations26 Jan 2022 Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed Chi, Minmin Chen

Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.

Data Augmentation Recommendation Systems

Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities

no code implementations6 May 2021 Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen

We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.

counterfactual Recommendation Systems

Batch Reinforcement Learning Through Continuation Method

no code implementations ICLR 2021 Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed Chi, Honglak Lee, Minmin Chen

Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.

reinforcement-learning Reinforcement Learning (RL)

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making

no code implementations NeurIPS 2019 Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans

We investigate batch policy optimization for cost-sensitive classification and contextual bandits---two related tasks that obviate exploration but require generalizing from observed rewards to action selections in unseen contexts.

Decision Making Multi-Armed Bandits

The Dynamics of Signal Propagation in Gated Recurrent Neural Networks

no code implementations25 Sep 2019 Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington

We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.

Quantifying Long Range Dependence in Language and User Behavior to improve RNNs

no code implementations23 May 2019 Francois Belletti, Minmin Chen, Ed H. Chi

Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data.

Language Modelling Sequential Recommendation +2

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs

no code implementations25 Jan 2019 Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington

We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.

Top-K Off-Policy Correction for a REINFORCE Recommender System

1 code implementation6 Dec 2018 Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed Chi

The contributions of the paper are: (1) scaling REINFORCE to a production recommender system with an action space on the orders of millions; (2) applying off-policy correction to address data biases in learning from logged feedback collected from multiple behavior policies; (3) proposing a novel top-K off-policy correction to account for our policy recommending multiple items at a time; (4) showcasing the value of exploration.

Recommendation Systems

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks

no code implementations ICML 2018 Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz

We develop a theory for signal propagation in recurrent networks after random initialization using a combination of mean field theory and random matrix theory.

Language Modelling

MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks

no code implementations18 Nov 2017 Minmin Chen

We introduce MinimalRNN, a new recurrent neural network architecture that achieves comparable performance as the popular gated RNNs with a simplified structure.

Efficient Vector Representation for Documents through Corruption

1 code implementation8 Jul 2017 Minmin Chen

The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks.

Document Classification Representation Learning +3

Marginalizing Corrupted Features

no code implementations27 Feb 2014 Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger

In this paper, we propose a third, alternative approach to combat overfitting: we extend the training set with infinitely many artificial training examples that are obtained by corrupting the original training data.

Bayesian Inference Test

Cost-Sensitive Tree of Classifiers

no code implementations9 Oct 2012 Zhixiang Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen

Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e. g. search engines and email spam filters).

Test

Co-Training for Domain Adaptation

no code implementations NeurIPS 2011 Minmin Chen, Kilian Q. Weinberger, John Blitzer

Our algorithm is a variant of co-training, and we name it CODA (Co-training for domain adaptation).

Domain Adaptation

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