摘要
Abstract
Social networks include the interest network taking the interest as core and the trust network taking the trust as core.The research focus of this paper is that how to use the projects data of the friends in social networks with similar trust and interest to expand the project dataset of user’s own,to alleviate the sparsity of user data,and to use the data of project rating score of target user’s friends to generate recommendation for it.Compared with traditional recommendation methods,the paper presents an improved SIMTM(Similar and Trust Model),which can provide more efficient recommendation experience.The model fuses interest and confidence as the initial intimacy, and makes recommendation according to the fused networks of friends,at the same time it constantly optimises the project rating score dataset according to the recommended feedbacks,this makes user’s close friends be more intimate while filtering out user’s ordinary friends,and optimises the association of interest and trust between user,moreover it re-calculates the intimacy degree between users to form a fusion network which fuses the user and user’s friends until the twice recommendation results before and the after derived from intimacy degree are close,and then constructs the fusion network based on the derived optimal intimacy degree for recommendation.Experimental results show that,the model can effectively improve the accuracy and coverage of recommendation of users,especially in the case of data sparsity.关键词
社会网络/兴趣网络/信任网络/融合网络/推荐反馈/信任更新Key words
Social network/Interested network/Trust network/Fusion network/Recommendation feedback/Trust update分类
信息技术与安全科学
