Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》
Abstract： The current trust-based recommendation algorithms did not fully exploit the trust relationship between users and it lacked reasonable trust transitivity rules, which greatly affected the reliability and accuracy of the recommendation algorithm. Aiming at the above problems, this paper combined user rating data with the user's social relationship to build a trust transitivity model, and proposes a recommendation algorithm based on trust transitivity. Firstly, the algorithm uses the score data to calculate the implicit direct trust relationship of the users in the trust transitivity model. Secondly, the indirect trust relationship of multiple trust transitivity chains is solved by solving the ordered weighted average operator. Finally, it converge the user’s trust and similarity into comprehensive similarity for predictive recommendation. The experimental results show that the proposed algorithm can effectively improve the recommendation quality of the system.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-04-01 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problems that the traditional recommendation algorithm ignores the influence of the tight structure of social network structure on user trust transmission and the lack of social psychological explanation, a recommendation algorithm based on link prediction for directed interaction and user trust is proposed. Firstly, the similar user circle of the target user is identified by the integrated similarity between the user preference behavior and the social circle. Secondly, the directional interaction influence between the target users is obtained by combining the node gravity index and the directed influence factor. The integrated user trust value of the directional interaction influence and the user score trust finds a trustworthy similar user set in the similar circle of friends of the target user, which effectively improves the accuracy of the recommendation and finally generates the recommendation. The results show that the proposed method has a significant improvement in performance compared to the previous social network recommendation algorithm.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-17 Cooperative journals: 《计算机应用研究》
Abstract： In view of the traditional recommendation algorithm ignoring the impact of social behavior of users, the incomprehensive research perspective and lack of physical explanation, a recommendation algorithm was proposed that integrated social behavior and tagging behavior of users. Firstly, the attractiveness between user nodes in social network was calculated by gravity model to measure the similarity of users' social behavior. Secondly, the user's favorite object model was constructed by label information, the gravitation formula was also used to calculate the gravitation between favorite objects to measure the similarity of tagging behavior. Finally, the paper introduced the variables to weigh the proportion of two similar values, and then got the set of neighbors and generated recommendations. Experimental results using Last. fm dataset showed that the proposed algorithm had higher precision and recall.
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