Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： In order to improve the accuracy of truth discovery in multi-truth scene, this paper proposed a multi-ant colonies synchronization optimization based multi-truth discovery (MAC-SO-MTD) algorithm. It modeled the multi-truth discovery problem as the subset problem, which goal was maximizing the weighted sum of similarity between the set of observations provided by each data source and the set of true values of the object. On this basis, then designed ant colony algorithm to solve the problem. It set ant colonies according to the number of objects. Based on the subset problem’s structure graph, this paper used routes’ probability transition equations to search for truths synchronically. After one cycle, the best route of this cycle updating and no updating were two instances of updating pheromone, which improved the convergence speed. Finally, the analysis of algorithm complexity and contrast experiment on the real data set validated the superiority of the algorithm.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： To further improve the performance of personalized tag recommendation, this paper argued that traditional methods ignore the potential and informative tags hidden in the context of users and items. Aimed at this, this paper proposed a novel personalized tag recommendation method BPR-PITF-P based on potential tag mining and fine-grained preference. Firstly, BPR-PITF-P leverages the context information of both users and items to mine potential and useful tags, and gets three kinds of tags: positive tags, potential tags, and negative tags. Based on the above, it translates the traditional pairwise preference into fine-grained preference relationship among user-item post and tags. This kind of treatment helps alleviate the sparse problem of tagging data. Second, combined with pairwise interaction tensor factorization method to predict preference value, BPR-PITF-P models the preference relationship based on the optimization criteria of Bayesian personalized ranking, and develops a personalized tag recommendation model followed by optimization algorithm. The comparison results show that our proposed method could improve tag recommendation performance in the premise of guarantee convergence speed.
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