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Improving group recommendations via detecting comprehensive correlative information

机译：通过检测全面的相关信息来改进小组推荐

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摘要

Traditionally, recommender systems are applied to recommending items to individual users. However, there has been a proliferation of recommender systems that try to make recommendations to user groups. Although several approaches were proposed to generate group recommendations, they made recommendations simply through aggregating individual ratings or individual predicted results, rather than comprehensively investigating the inherent relationships between members and the group, which can be used to improve the performance of group recommender systems. For this reason, these approaches continue to suffer from data sparsity and do not work well for recommending items to user groups. Therefore, we proposed a new approach for group recommendations based on random walk with restart (RWR) method. The goal of the work in this paper is describing groups' preferences better by comprehensively detecting the correlative information among users, groups, and items, in order to alleviate the data sparsity problem and improve the performance of group recommender systems. In the proposed approach, we represent the relationships among users, groups, and items as a tripartite graph. Based on the tripartite graph, RWR can predict the relevance degrees between groups and unrated items by comprehensively detecting their relationships. Using these relevance degrees, we can describe a group's preferences better so as to achieve a more accurate recommendation. In particular, we devised two recommendation algorithms based on different recommendation strategies. Finally, we conducted experiments to evaluate our method and compare it with other state-of-the-art methods using the real-world CAMRa2011 data-set. The results show the advantage of our approach over comparative ones.
机译：传统上，推荐系统用于向单个用户推荐项目。但是，推荐系统的数量激增，它们试图向用户组提出建议。尽管提出了几种生成小组建议的方法，但是它们只是通过汇总单个评分或单个预测结果来提出建议，而不是全面调查成员与小组之间的内在联系，这可以用来提高小组推荐系统的性能。因此，这些方法继续遭受数据稀疏性的困扰，并且不能很好地向用户组推荐项目。因此，我们提出了一种基于随机游动重新启动（RWR）方法的团体推荐新方法。本文的工作目标是通过全面检测用户，组和项目之间的相关信息来更好地描述组的偏好，以减轻数据稀疏性问题并提高组推荐系统的性能。在提出的方法中，我们将用户，组和项目之间的关系表示为三方图。基于三方图，RWR可以通过全面检测它们之间的关系来预测组和未评级项目之间的相关度。使用这些相关度，我们可以更好地描述组的偏好，从而获得更准确的推荐。特别是，我们根据不同的推荐策略设计了两种推荐算法。最后，我们进行了实验以评估我们的方法，并使用真实的CAMRa2011数据集将其与其他最新方法进行比较。结果显示了我们的方法优于比较方法的优势。

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