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A multistep priority-based ranking for top-N recommendation using social and tag information
Journal of Ambient Intelligence and Humanized Computing ( IF 3.662 ) Pub Date : 2020-08-04 , DOI: 10.1007/s12652-020-02388-y
Suman Banerjee , Pratik Banjare , Bithika Pal , Mamata Jenamani

Recommender system is a collection of information retrieval tools and techniques used for recommending items to users based on their choices. For improving recommendation accuracy, the use of extra information (e.g., social, trust, item tags, etc.) other than user-item rating data remains an active area of research since last one and half decade. In this paper, we propose a novel methodology for top-N item recommendation, which uses three different kinds of information: user-item rating data, social network among the users, and tags associated with the items. The proposed method has mainly five steps: (i) creation of neighbor users’ item set, (ii) construction of the user-feature matrix, (iii) computation of user priority, (iv) computation of item priority, and finally, (v) recommendation based on the item priority. We implement the proposed methodology with three recommendation dataset. We compare our results with that of the obtained from some state-of-the-art ranking methods and observe that recommendation accuracy is improved in the case of the proposed algorithm for both all users and cold-start users scenarios. The algorithm is also able to generate more cold-start items in the recommended item list.



中文翻译:

使用社交和标签信息对前N个推荐进行基于多步骤优先级的排序

推荐系统是信息检索工具和技术的集合,用于根据用户的选择向用户推荐项目。为了提高推荐的准确性,自上个十年以来,使用用户项目评分数据以外的其他信息(例如社交,信任,项目标签等)仍然是研究的活跃领域。在本文中,我们提出了一种用于前N个项目推荐的新颖方法,该方法使用三种不同类型的信息:用户项目评分数据,用户之间的社交网络以及与项目相关联的标签。所提出的方法主要包括五个步骤:(i)创建邻居用户的项目集,(ii)构建用户特征矩阵,(iii)计算用户优先级,(iv)计算项目优先级,最后,( v)基于项目优先级的建议。我们使用三个推荐数据集来实施所提出的方法。我们将我们的结果与从一些最新的排名方法获得的结果进行比较,并观察到在所有用户和冷启动用户场景下,所提算法的推荐准确性都得到了提高。该算法还能够在推荐项目列表中生成更多冷启动项目。

更新日期:2020-08-04
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