Abstract
Social 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.
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Acknowledgments
This work was supported by the Natural Science Foundation of China (No. 61533018), the National Basic Research Program of China (No. 2014CB340503) and the National Natural Science Foundation of China (No. 61272332 and 61602479). And this work was also supported by Google through focused research awards program.
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Wei, Z., Zhao, J., Liu, K., He, S. (2016). Link Prediction via Mining Markov Logic Formulas to Improve Social Recommendation. In: Chen, H., Ji, H., Sun, L., Wang, H., Qian, T., Ruan, T. (eds) Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data. CCKS 2016. Communications in Computer and Information Science, vol 650. Springer, Singapore. https://doi.org/10.1007/978-981-10-3168-7_14
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