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| A Novel Recommendation Approach of Electronic Literature Resources Combining Semantic and Social Features |
| Yang Chen1, Liu Tingting1, Liu Lei1, Niu Ben1, Sun Jianshan2 |
1.College of Management, Shenzhen University, Shenzhen 518060
2.School of Management, Hefei University of Technology, Hefei 230009 |
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Abstract With the arrival of the era of information expansion, the load on electronic literature databases will dramatically increase, and it will become increasingly difficult for users to search for their required pieces of literature. In response to this issue, the development of recommendation systems to assist the management of electronic literature databases has received extensive attention from researchers. Currently, one commonly used recommendation technique for literature databases is collaborative filtering. However, the traditional collaborative filtering algorithms, which only consider the similarity of users’ search-history, ignore several important factors, such as the users’ semantic similarity and social relationships. In this paper, we integrated a text content similarity based on topic model as well as two kinds of user similarities based on social relationships (user tag similarity and personal group similarity) into the user collaborative filtering recommender system by utilizing an unsupervised integration strategy. The experiment on the real data set shows that by adding the multiple source features, there is an enhancement and promotion effect on the recommendation accuracy, which provides strong implications for related electronic literature resource recommendation research in the future.
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Received: 08 January 2019
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