Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders

no code implementations • 23 Aug 2023 • Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi

In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback.

counterfactual Recommendation Systems +1

Recency Dropout for Recurrent Recommender Systems

no code implementations • 26 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

ShopTalk: A System for Conversational Faceted Search

no code implementations • 2 Sep 2021 • Gurmeet Manku, James Lee-Thorp, Bhargav Kanagal, Joshua Ainslie, Jingchen Feng, Zach Pearson, Ebenezer Anjorin, Sudeep Gandhe, Ilya Eckstein, Jim Rosswog, Sumit Sanghai, Michael Pohl, Larry Adams, D. Sivakumar

The dialog understanding system consists of a deep-learned Contextual Language Understanding module, which interprets user utterances, and a primarily rules-based Dialog-State Tracker (DST), which updates the dialog state and formulates search requests intended for the fulfillment engine.

Management slot-filling +1