Silence is also evidence: interpreting dwell time for recommendation from psychological perspective

Peifeng Yin, Ping Luo, Wang-Chien Lee, Min Wang
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引用次数: 62

Abstract

Social media is a platform for people to share and vote content. From the analysis of the social media data we found that users are quite inactive in rating/voting. For example, a user on average only votes 2 out of 100 accessed items. Traditional recommendation methods are mostly based on users' votes and thus can not cope with this situation. Based on the observation that the dwell time on an item may reflect the opinion of a user, we aim to enrich the user-vote matrix by converting the dwell time on items into users' ``pseudo votes'' and then help improve recommendation performance. However, it is challenging to correctly interpret the dwell time since many subjective human factors, e.g. user expectation, sensitivity to various item qualities, reading speed, are involved into the casual behavior of online reading. In psychology, it is assumed that people have choice threshold in decision making. The time spent on making decision reflects the decision maker's threshold. This idea inspires us to develop a View-Voting model, which can estimate how much the user likes the viewed item according to her dwell time, and thus make recommendations even if there is no voting data available. Finally, our experimental evaluation shows that the traditional rate-based recommendation's performance is greatly improved with the support of VV model.
沉默也是证据:从心理学角度解释推荐的停留时间
社交媒体是人们分享和投票内容的平台。通过对社交媒体数据的分析,我们发现用户在评分/投票方面相当不活跃。例如,用户平均只在100个访问项目中投票2个。传统的推荐方法大多基于用户的投票,无法应对这种情况。基于观察到商品的停留时间可能反映用户的意见,我们的目标是通过将商品的停留时间转换为用户的“伪投票”来丰富用户投票矩阵,从而帮助提高推荐性能。然而,由于许多主观的人为因素,如用户期望,对各种物品质量的敏感性,阅读速度,都涉及到在线阅读的随意行为,因此正确解释停留时间是具有挑战性的。在心理学中,人们在做决策时假定有选择阈值。花在决策上的时间反映了决策者的门槛。这个想法启发我们开发了一个View-Voting模型,它可以根据用户的停留时间来估计用户对所看物品的喜爱程度,从而在没有投票数据的情况下进行推荐。最后,我们的实验评估表明,在VV模型的支持下,传统的基于率的推荐性能得到了很大的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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