CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Link Prediction via Mining Markov Logic Formulas to Improve Social Recommendation
Wei Zhuoyu; Zhao Jun; Liu Kang; He Shizhu
2016-09
Conference Name全国知识图谱与语义计算 大会
Conference Date2016-9
Conference Place北京
AbstractSocial networks have been a main way to obtain information in recent years, but the huge amount of information obstructs people from obtaining something that they are really interested in. Social recommendation system is introduced to solve this problem and brings a new challenge of predicting peoples preferences. In a graph view, social recommendation can be viewed as link prediction task on the social graph. Therefore, some link prediction technique can apply to social recommendation. In this paper, we propose a novel approach to bring logic formulas in social recommendation system and it can improve the accuracy of recommendations. This approach is made up of two parts: (1) It treats the whole social network with kinds of attributes as a semantic network, and finds frequent structures as logic formulas via random graph algorithms. (2) It builds a Markov Logic Network to model logic formulas, attaches weights to each of them to measure formulas contributions, and then learns the weights discriminatively from training data. In addition, the formulas with weights can be viewed as the reason why people should accept a specific recommendation, and supplying it for people may increase the probability of people accepting the recommendation. We carry out several experiments to explore and analyze the effects of various factors of our method on recommendation results, and get the final method to compare with baselines. 
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14496
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorLiu Kang
AffiliationNational Laboratory of Pattern Recognition,Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wei Zhuoyu,Zhao Jun,Liu Kang,et al. Link Prediction via Mining Markov Logic Formulas to Improve Social Recommendation[C],2016.
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