Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (3): 9-21    DOI: 10.11925/infotech.2096-3467.2017.1123
Current Issue | Archive | Adv Search |
Classification Recommendation Based on ESSVM
Jun Hou1,2,3(),Kui Liu1,Qianmu Li2
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)
Download: PDF(2792 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      
Abstract  

[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.

Key wordsContext Sensitive Interest Points      Embedded Space Ranking      SVM      Recommendation Algorithm     
Received: 23 November 2017      Published: 03 April 2018

Cite this article:

Jun Hou,Kui Liu,Qianmu Li. Classification Recommendation Based on ESSVM. Data Analysis and Knowledge Discovery, 2018, 2(3): 9-21.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1123     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I3/9

[1] 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.
[2] Linden G, Smith B, York J.Amazon.com Recommendations: Item-to-Item Collaborative Filtering[J]. IEEE Internet Computing, 2003, 7(2): 76-80.
[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.
[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.
[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.
[1] Bengong Yu,Yangnan Chen,Ying Yang. Classifying Short Text Complaints with nBD-SVM Model[J]. 数据分析与知识发现, 2019, 3(5): 77-85.
[2] Zixuan Zhang,Hao Wang,Liping Zhu,Sanhong eng. Identifying Risks of HS Codes by China Customs[J]. 数据分析与知识发现, 2019, 3(1): 72-84.
[3] Yang Zhao,Qiqi Li,Yuhan Chen,Wenhang Cao. Examining Consumer Reviews of Overseas Shopping APP with Sentiment Analysis[J]. 数据分析与知识发现, 2018, 2(11): 19-27.
[4] Shihai Tian,Deli Lyu. An Early Warning Algorithm for Public Opinion of Safety Emergency[J]. 数据分析与知识发现, 2017, 1(2): 11-18.
[5] Liu Hongguang,Ma Shuanggang,Liu Guifeng. Classifying Chinese News Texts with Denoising Auto Encoder[J]. 现代图书情报技术, 2016, 32(6): 12-19.
[6] Wang Yong,Deng Jiangzhou,Deng Yongheng,Zhang Pu. A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J]. 现代图书情报技术, 2016, 32(6): 73-79.
[7] Ce Zhang,Yuncheng Du,Ran Liang. A Study on Hub Page Recognition Using URL Features[J]. 现代图书情报技术, 2016, 32(1): 24-31.
[8] Tang Xiangbin, Lu Wei, Zhang Xiaojuan, Huang Shihao. Feature Analysis and Automatic Identification of Query Specificity[J]. 现代图书情报技术, 2015, 31(2): 15-23.
[9] Hu Jiming, Chen Guo. Study on Improvement of Text Classification Using HS-SVM[J]. 现代图书情报技术, 2014, 30(9): 74-80.
[10] Wang Weijun, Song Meiqing. A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences[J]. 现代图书情报技术, 2014, 30(6): 25-32.
[11] Yuan Fuyong, Cai Honglei. A Recommendation Algorithm Based on Random Walk in Trust Network[J]. 现代图书情报技术, 2014, 30(10): 70-75.
[12] Liu Kan, Zhu Huaiping, Liu Xiuqin. Detection of Internet Deceptive Opinion Based on SVM[J]. 现代图书情报技术, 2013, 29(11): 75-80.
[13] Li Xiao, Ding Shengchun. Research on Review Spam Recognition[J]. 现代图书情报技术, 2013, 29(1): 63-68.
[14] Xu Jian, Wen Haosheng. Study on Talents Description Web Page Automatic Recognition System[J]. 现代图书情报技术, 2011, 27(6): 20-26.
[15] Ying Wei,Wang Zhengou,An Jinlong. Research on Two Classes Text Categorization Method Based on an Improved Support Vector Machine[J]. 现代图书情报技术, 2005, 21(12): 44-47.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn