Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
| Autor: | Deng Cai, Ziyu Guan, Junxiong Zhu, Beidou Wang, Wenwu Ou, Yu Gong, Yingcai Ma, Qingwen Liu, Yu Zhu |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Focus (computing) Information retrieval Computer science Rank (computer programming) 02 engineering and technology Recommender system Computer Science - Information Retrieval Machine Learning (cs.LG) Task (computing) 020204 information systems 0202 electrical engineering electronic engineering information engineering Code (cryptography) Key (cryptography) 020201 artificial intelligence & image processing Information Retrieval (cs.IR) PATH (variable) |
| Zdroj: | CIKM |
| DOI: | 10.1145/3357384.3357805 |
| Popis: | Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively intervene the recommendation results rather than passively receive them, and (2) the system learns more about users so as to provide better recommendation. We focus on the single-round interaction, i.e. the system asks the user a question (Step 1), and exploits his feedback to generate better recommendation (Step 2). A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2. We achieve this by transforming Step 1 into a query recommendation task and Step 2 into a retrieval task. The former task is our key challenge. We firstly propose a model based on Meta-Path to efficiently retrieve hundreds of query candidates from the large query pool. Then an adapted Attention-GRU model is developed to effectively rank these candidates for recommendation. Offline and online experiments on Taobao, a large-scale e-commerce platform in China, verify the effectiveness of our interactive system. The system has already gone into production in the homepage of Taobao App since Nov. 11, 2018 (see https://v.qq.com/x/page/s0833tkp1uo.html on how it works online). Our code and dataset are public in https://github.com/zyody/QueryQR. Comment: 9 pages, 6 figures, submitted to CIKM 2019 Applied Research Track |
| Databáze: | OpenAIRE |
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