1(School of Marxism Studies, Nanjing University of Science and Technology, Nanjing 210094, China) 2(School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China) 3(Zijin College, Nanjing University of Science and Technology, Nanjing 210094, China)
[Objective] The traditional interest point recommendation methods are mostly based on simple context and can only recommend objects that are the most popular, cheapest or the closest to interest points. Combines time, category information with user’s check-in records, and make up for the shortcomings of traditional interest points recommendation methods with characteristics of user’s preference, and provide support for improving recommendation accuracy. [Methods] The interest point recommendation is considered as a sorting problem. In this paper, ESSVM (Embedded space ranking SVM) is proposed based on embedded spatial sorting support vector machine model to classify interest points according to different features. User preferences are captured using check-in data, and machine learning models are used to adjust the importance of different attributes in sorting. [Results] Compared with UserCF, VenueCF, PoV, NNR and other recommendation methods, ESSVM not only can capture individual heterogeneous preferences, but also can reduce the consumption of the training model of time. [Limitations] Collecting and integrating different contextual information from different location based social networks (LBSNs) will take a lot of work. In addition, if users reduce the granularity of time and class in ESSVM, they maybe need to solve the problem of data sparseness. [Conclusions] This method takes account of the impact of time variation on user preferences, as well as the location categories that users visit at different times. By providing useful contextual information and check-in records, it provides personalized suggestions.
Chen Z, Li T, Sun Y.A Learning Approach to SQL Query Results Ranking Using Skyline and Users’ Current Navigational Behavior[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(5): 2683-2693.
doi: 10.1109/TKDE.2012.128
[2]
Linden G, Smith B, York J.Amazon.com Recommendations: Item-to-Item Collaborative Filtering[J]. IEEE Internet Computing, 2003, 7(2): 76-80.
doi: 10.1109/MIC.2003.1167344
[3]
Noulas A, Scellato S, Lathia N, et al.A Random Walk Around the City: New Venue Recommendation in Location-based Social Networks[C]//Proceedings of the 11th International Confernece on Privacy, Security, Risk and Trust. 2012.
[4]
Bao J, Zheng Y, Mokbel M F.Location-based and Preference- aware Recommendation Using Sparse Geo-Social Networking Data[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems. 2012.
[5]
Zhu H, Chen E, Xiong H, et al.Mining Mobile User Preferences for Personalized Context-aware Recommendation[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2015, 21(5): 58-75.
doi: 10.1145/2532515
[6]
Yao L, Sheng Q Z, Qin Y, et al.Context-aware Point-of- Interest Recommendation Using Tensor Factorization with Social Regularization[C] // Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2015.
[7]
Ying Y, Chen L, Chen G.A Temporal-Aware POI Recommendation System Using Context-aware Tensor Decomposition and Weighted HITS[J]. Neurocomputing, 2017, 242: 195-205.
doi: 10.1016/j.neucom.2017.02.067
[8]
Ricci F, Rokach L, Shapira B.Introduction to Recommender Systems Handbook[M]. Springer, 2012.
[9]
Samworth R J.Optimal Weighted Nearest Neighbour Classifiers[J]. The Annals of Statistics, 2012, 40(6): 2733-2763.
doi: 10.1214/12-AOS1049
