A Trust Based Recommender System by Analyzing User Comments of Social Network

Autor: Sheng-Han Yang, 楊昇翰
Rok vydání: 2016
Druh dokumentu: 學位論文 ; thesis
Popis: 104
Due to the repaid growth of Internet, the recommender systems are widely used as the network service. Recommender system can help user reducing the search cost and providing a list of suitable items for the user. In the past study, Collaborative Filtering Recommendation (CF) has been widely applied and successfully used in the Internet. Now, with the popularity of social network, many researchers have proposed using trust between users for collaborative filtering recommendation. This kind of approach we also call it as the Trust-based Recommender System (TBRS). And, one of the important issues about TBRS is how to find and define the optimal trust values. In the past study, the initial trust value is usually set by a random number or directly set by a certain value. The initial trust values shall represent positive or negative relationship between users. However, the positive/negative relationship of initial trust values did not considered in the past studies. In order to find out the positive/negative relationship, this research analyzed the users’ comment of social network. The features of these comments are analyzed and derived by using Term Frequency-Inverse Documents Frequency (TF-IDF) method. To obtain the training model for finding the positive/negative relationship, these features are then classified by using Support Vector Machine (SVM). The obtained Trust Model and the Trust Propagation Model are then integrated to generate the user’s trust network model. This research has proposed a trust based recommender system by analyzing user comments of social network. The proposed method improves the finding of initial trust value. From the experiments results, it proves that the proposed method has better prediction outcome than other methods.
Databáze: Networked Digital Library of Theses & Dissertations
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