基于用户关系的兴趣点推荐算法
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摘要
基于用户的协同过滤算法能够度量用户的兴趣爱好,被广泛地应用于兴趣点推荐中,但该算法没有考虑到用户间的关系,导致兴趣挖掘不够准确。本文在基于用户的协同过滤算法的基础上,引入用户的签到数量及用户共同签到数量来刻画用户关系,并设计了基于用户关系的兴趣点推荐算法。试验表明,该算法能够有效提高兴趣点的推荐精度。
The collaborative filtering recommendation algorithm can reflect the degree of user's interest, which has been widely used in the point of interest recommendation. However, it doesn't consider the user relationship, resulting in the interesting mining is not precise enough. Based on the collaborative filtering recommendation algorithm, this paper uses the number of user check-ins and the number of common check-ins between different users to characterize the user relationship, designs the point of interest recommendation based on user relationship. The experimental results show that, the proposed algorithm can improve the accuracy of point of interest recommendation.
The collaborative filtering recommendation algorithm can reflect the degree of user's interest, which has been widely used in the point of interest recommendation. However, it doesn't consider the user relationship, resulting in the interesting mining is not precise enough. Based on the collaborative filtering recommendation algorithm, this paper uses the number of user check-ins and the number of common check-ins between different users to characterize the user relationship, designs the point of interest recommendation based on user relationship. The experimental results show that, the proposed algorithm can improve the accuracy of point of interest recommendation.
引文
[1] Ference G,Ye M,Lee W C.Location recommendation for out-of-town users in location-based social networks[C]// ACM International Conference on Information & Knowledge Management.ACM,2013:721-726.
[2] He J,Li X,Liao L,et al.Inferring a personalized next point-of-interest recommendation model with latent behavior patterns[C]// Thirtieth AAAI Conference on Artificial Intelligence.AAAI Press,2016:137-143.
[3] 吴毅涛,张兴明,王兴茂,等.基于用户模糊相似度的协同过滤算法[J].通信学报,2016,37(1):198-206.
[4] Ye M,Yin P,Lee W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]// ACM,2011:325-334.
[5] Yuan Q,Cong G,Ma Z,et al.Time-aware point-of-interest recommendation[C]// International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2013:363-372.
[6] Chen X,Zeng Y,Cong G,et al.On information coverage for location category based point-of-interest recommendation[C]// Twenty-Ninth AAAI Conference on Artificial Intelligence.AAAI Press,2015:37-43.
[7] Adomavicius G,Tuzhilin A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge & Data Engineering,2005,17(6):734-749.
[8] Yuan Q,Yuan Q,Yuan Q,et al.An experimental evaluation of point-of-interest recommendation in location-based social networks[J].Proceedings of the Vldb Endowment,2017,10(10):1 010-1 021.
[9] 朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.
[2] He J,Li X,Liao L,et al.Inferring a personalized next point-of-interest recommendation model with latent behavior patterns[C]// Thirtieth AAAI Conference on Artificial Intelligence.AAAI Press,2016:137-143.
[3] 吴毅涛,张兴明,王兴茂,等.基于用户模糊相似度的协同过滤算法[J].通信学报,2016,37(1):198-206.
[4] Ye M,Yin P,Lee W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]// ACM,2011:325-334.
[5] Yuan Q,Cong G,Ma Z,et al.Time-aware point-of-interest recommendation[C]// International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2013:363-372.
[6] Chen X,Zeng Y,Cong G,et al.On information coverage for location category based point-of-interest recommendation[C]// Twenty-Ninth AAAI Conference on Artificial Intelligence.AAAI Press,2015:37-43.
[7] Adomavicius G,Tuzhilin A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge & Data Engineering,2005,17(6):734-749.
[8] Yuan Q,Yuan Q,Yuan Q,et al.An experimental evaluation of point-of-interest recommendation in location-based social networks[J].Proceedings of the Vldb Endowment,2017,10(10):1 010-1 021.
[9] 朱郁筱,吕琳媛.推荐系统评价指标综述[J].电子科技大学学报,2012,41(2):163-175.