最大化语义量提高推荐多样性

Atom Sonoda, F. Toriumi, Hiroto Nakajima
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引用次数: 0

摘要

通过电子媒介传播的信息量正在增加,目前正在引进推荐系统。但是,有人指出,由于过度推荐,存在过滤气泡和回音室等问题,给用户提供了有偏差的信息。在之前的研究中,我们讨论了基于文章多样性的用户行为变化。在本研究中,我们提出了一个推荐系统,该系统引入了一种机制来提高文章的语言多样性,并通过实验证明了该系统能够推荐各种各样的文章。我们也明确了仅仅展示各种各样的文章不足以提高用户点击文章的多样性的条件,需要推荐能够抓住用户兴趣的文章。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Recommendation Diversity by Maximizing Semantic Volume
The amount of information transmitted via electronic media is increasing, and recommender systems are being introduced. However, it has been pointed out that there are problems such as filter bubbles and echo chambers, which provide users with biased information due to excessive recommendations. In previous studies, we have discussed changes in user behavior based on the diversity of articles. In this study, we propose a recommender system that introduces a mechanism to improve the linguistic diversity of articles, and show through experiments that the system is able to recommend a variety of articles. We also have clarified the condition that merely displaying a variety of articles is not sufficient to improve the diversity of articles clicked by users, and that it is necessary to recommend articles that capture the interests of users.
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