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Behavior-Based Approach for User Interests Prediction

机译：基于行为的用户兴趣预测方法

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摘要

Due to the emergence and the prevalence of social networks, social interactions are found beneficial for Recommender Systems. Obviously, users can now rate items, comment and suggest them to friends through social networks. Therefore, these users behaviors must be integrated to predict her preferences. However, most of the works proposed in the literature integrate only users ratings in recommendation process and ignore other behaviors made by users while/after seeing an item. In this paper, we propose a new approach that integrates in a generic way all the user behaviors in order to predict her interests. We conduct a comprehensive effectiveness evaluation on real dataset crawled from Pinhole platform. We consider several social behaviors such as comment, time spent, recommendations and shares. We evaluate the impact of each behavior in the prediction accuracy. Experimental results demonstrate the importance of all social behaviors and the effectiveness of our approach compared to collaborative filtering rating-based and time-spent-based approaches.
机译：由于社交网络的出现和普及，发现社交互动对推荐系统很有用。显然，用户现在可以通过社交网络对项目进行评分，评论并推荐给朋友。因此，必须综合这些用户的行为以预测其偏好。然而，文献中提出的大多数作品在推荐过程中仅整合了用户评分，而忽略了用户在查看项目时/之后所做出的其他行为。在本文中，我们提出了一种新方法，该方法以通用方式集成了所有用户行为，以预测其兴趣。我们对从Pinhole平台抓取的真实数据集进行了全面的有效性评估。我们考虑几种社交行为，例如评论，花费的时间，推荐和分享。我们评估每种行为对预测准确性的影响。实验结果表明，与基于协作过滤基于评分和基于时间的方法相比，所有社会行为的重要性以及我们方法的有效性。

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