https://scholars.lib.ntu.edu.tw/handle/123456789/632628
標題: | Practical Counterfactual Policy Learning for Top-K Recommendations | 作者: | Liu, Yaxu Yen, Jui Nan Yuan, Bowen Shi, Rundong Yan, Peng CHIH-JEN LIN |
關鍵字: | counterfactual learning | policy learning | recommender systems | selection bias | 公開日期: | 14-八月-2022 | 來源出版物: | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | 摘要: | For building recommender systems, a critical task is to learn a policy with collected feedback (e.g., ratings, clicks) to decide which items to be recommended to users. However, it has been shown that the selection bias in the collected feedback leads to biased learning and thus a sub-optimal policy. To deal with this issue, counterfactual learning has received much attention, where existing approaches can be categorized as either value learning or policy learning approaches. This work studies policy learning approaches for top-K recommendations with a large item space and points out several difficulties related to importance weight explosion, observation insufficiency, and training efficiency. A practical framework for policy learning is then proposed to overcome these difficulties. Our experiments confirm the effectiveness and efficiency of the proposed framework. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/632628 | ISBN: | 9781450393850 | DOI: | 10.1145/3534678.3539295 |
顯示於: | 資訊工程學系 |
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