Recommendations based on user effective point-of-interest path

Point-of-interest (POI) recommendation has become an important service in location-based social networks. Existing recommendation algorithms provide users with a diverse pool of POIs. However, these algorithms tend to generate a list of unrelated POIs that user cannot continuously visit due to lack of appropriate associations. In this paper, we first proposed a concept that can recommend POIs by considering both category diversity features of POIs and possible associations of POIs. Then, we developed a top-k POI recommendation model based on effective path coverage. Moreover, considering this model has been proven to be a NP-hard problem, we developed a dynamic optimization algorithm to provide an approximate solution. Finally, we compared it with two popular algorithms by using two real-world datasets, and found that our proposed algorithm has better performance in terms of diversity and precision.

[1]  Jian Wu,et al.  User Clustering Based Social Network Recommendation: User Clustering Based Social Network Recommendation , 2014 .

[2]  Chen Ke.  User Clustering Based Social Network Recommendation , 2013 .

[3]  Juan-Zi Li,et al.  Typicality-Based Collaborative Filtering Recommendation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[4]  Yang Yang,et al.  Multitask Spectral Clustering by Exploring Intertask Correlation , 2015, IEEE Transactions on Cybernetics.

[5]  Jie Zhang,et al.  Leveraging Decomposed Trust in Probabilistic Matrix Factorization for Effective Recommendation , 2014, AAAI.

[6]  Li Guo,et al.  Combining Heterogenous Social and Geographical Information for Event Recommendation , 2014, AAAI.

[7]  Krzysztof Janowicz,et al.  On the semantic annotation of places in location-based social networks , 2011, KDD.

[8]  Xing Xie,et al.  Content-Based Collaborative Filtering for News Topic Recommendation , 2015, AAAI.

[9]  Shengchao Qin,et al.  On Information Coverage for Location Category Based Point-of-Interest Recommendation , 2015, AAAI.

[10]  Chi-Yin Chow,et al.  iGeoRec: A Personalized and Efficient Geographical Location Recommendation Framework , 2015, IEEE Transactions on Services Computing.

[11]  Haisu Zhang,et al.  Group Interests and Their Correlations Mining Based on Wikipedia: Group Interests and Their Correlations Mining Based on Wikipedia , 2011 .

[12]  Zhang Hai,et al.  Group Interests and Their Correlations Mining Based on Wikipedia , 2011 .

[13]  Samir Khuller,et al.  The Budgeted Maximum Coverage Problem , 1999, Inf. Process. Lett..

[14]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

[15]  Jie Zhang,et al.  TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation , 2014, AAAI.

[16]  Hui Xiong,et al.  Learning geographical preferences for point-of-interest recommendation , 2013, KDD.

[17]  Li Yan,et al.  Extension of local association rules mining algorithm based on apriori algorithm , 2014, 2014 IEEE 5th International Conference on Software Engineering and Service Science.

[18]  Liang Wang,et al.  COT: Contextual Operating Tensor for Context-Aware Recommender Systems , 2015, AAAI.

[19]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[20]  Jianmin Wang,et al.  A Personalized Interest-Forgetting Markov Model for Recommendations , 2015, AAAI.

[21]  Jinsong Zhang,et al.  Recommending Nearby Strangers Instantly Based on Similar Check-In Behaviors , 2015, IEEE Transactions on Automation Science and Engineering.

[22]  Shuai Li,et al.  An Efficient Approach to Generating Location-Sensitive Recommendations in Ad-hoc Social Network Environments , 2015, IEEE Transactions on Services Computing.

[23]  Ke Wang,et al.  Are Features Equally Representative? A Feature-Centric Recommendation , 2015, AAAI.

[24]  Jiajun Bu,et al.  Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems , 2014, AAAI.

[25]  Mao Ye,et al.  Exploring social influence for recommendation: a generative model approach , 2012, SIGIR '12.