Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction | |
Chen, Jingwu2,3; Zhuang, Fuzhen2,3; Wang, Tianxin2,3; Lin, Leyu1; Xia, Feng1; Du, Lihuan1; He, Qing2,3 | |
刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
2021-07-01 | |
卷号 | 33期号:7页码:2903-2913 |
关键词 | Task analysis Predictive models Lifting equipment Mobile applications Collaboration User experience Article recommendation dwell time prediction click-guide network |
ISSN号 | 1041-4347 |
DOI | 10.1109/TKDE.2019.2960693 |
英文摘要 | In article recommendation, the amount of time user spends on viewing articles, dwell time, is an important metric to measure the post-click engagement of user on content and has been widely used as a proxy to user satisfaction, complementing the click feedback. Recently, the sequential pattern of impression-click-read has become one of the most popular type of article recommendation service in real world, where users are presented with a list of titles at first, then get interested in one and click in for reading. Predicting dwell time in such service is conditioned on the click, since the user reads the article only after he clicks the corresponding title. We argue that conventional models for dwell time prediction, which mainly focus on the relevance between the content and the general preference of user, are not well-designed for such service. There is a natural assumption in recommendation system that the click indicates user's getting attracted by the item. Therefore, in the pattern of impression-click-read, the user might get interested and curious on some other concepts different from his general preference while reading, due to the attraction of the title. Conventional models tend to ignore the gap between such temporary interest and the general preference of user in the reading behavior, which fails to use the pattern of impression-click-read and the assumption of the click very well. In this work, we propose a framework, Click-guide Network (CGN) for dwell time prediction, which makes good use of the sequential pattern and the assumption to model the "guidance" of the click on user preference. CGN is a joint learner for dwell time and click through rate (CTR). We introduce the CTR task as an auxiliary task to help us better learn the preference of user and the representation of title. Besides, we propose the Guider to capture the user's temporary interest raised by the title. We collect the data from WeChat, a widely-used mobile app in China, for experiments. The results demonstrate the advantages of CGN over several competitive baselines on dwell time prediction, while our case studies show how the Guider effectively capture the temporary interest of user. |
资助项目 | National Key Research and Development Program of China[2018YFB1004300] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[U1836206] ; Project of Youth Innovation Promotion Association CAS[2017146] ; WeChat cooperation project |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | IEEE COMPUTER SOC |
WOS记录号 | WOS:000658333300007 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/17612] |
专题 | 中国科学院计算技术研究所 |
通讯作者 | Chen, Jingwu |
作者单位 | 1.Tencent, WeChat Search Team, Beijing 100080, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, CAS, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Jingwu,Zhuang, Fuzhen,Wang, Tianxin,et al. Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(7):2903-2913. |
APA | Chen, Jingwu.,Zhuang, Fuzhen.,Wang, Tianxin.,Lin, Leyu.,Xia, Feng.,...&He, Qing.(2021).Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(7),2903-2913. |
MLA | Chen, Jingwu,et al."Follow the Title Then Read the Article: Click-Guide Network for Dwell Time Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.7(2021):2903-2913. |
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