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An Empirical Study On Effectiveness Of Temporal Informationas Implicit Ratings

机译：时间信息作为内隐评价有效性的实证研究

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摘要

Collaborative filtering is a widely used and proven method of building recommender systems, which provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an especially important factor in some e-commerce environments (such as a mobile environment, due to limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy.rnThis paper presents a variety of temporal information including item launch time, user buying time, the time difference between the two, as well as several combinations of these three. We conducted an empirical study on how temporal information affects the accuracy of a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results show the degree of effectiveness of a variety of temporal information. The empirical results give insight on how to incorporate temporal information to maximize the effectiveness of collaborative filtering in various e-commerce environments.
机译：协作过滤是构建推荐系统的一种广泛使用且经过验证的方法，它可以根据用户的明确评分提供有关产品或服务的个性化推荐。在某些电子商务环境（例如移动环境，由于连接时间和设备尺寸有限）中，推荐准确性成为特别重要的因素。随着用户偏好随时间的变化，时间信息可以提高推荐的准确性。本文介绍了各种时间信息，包括项目启动时间，用户购买时间，两者之间的时间差以及这三者的几种组合。我们对时间信息如何影响协作过滤系统（在移动电子商务环境中推荐字符图像（墙纸））的准确性进行了实证研究。实证结果表明各种时间信息的有效性程度。实证结果提供了有关如何整合时间信息以最大程度地提高各种电子商务环境中协作过滤效率的见解。

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