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
Hou Jun1,2,3(), Liu Kui1, Li Qianmu2
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 ( 4
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
ZTFLH:  TP391  

Cite this article:

Hou Jun,Liu Kui,Li Qianmu. 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

方法 优点 缺点
Random Walk[7] 注重用户先前没去过的地方; 考虑社会关系和兴趣点访问数据 有限的上下文信息;
假设性太强
Geo-Social Network[2] 利用指定的地理位置; 对未
访问场所的有效建议
冷启动问题;
缺乏时间背景
MGM[3] 模拟用户check-in行为的地
理影响; 结合用户的社会信
息和地理影响
极弱的稀疏频率数据;
简单的上下文信息;
忽略用户在时间效应
上的偏好变化
CIAP[4] 利用来自各个设备的上下
文日志开发上下文感知偏
好; 结合普遍偏好和个体
偏好
花费大量时间处理和
分析大量上下文日志; 忽视个人隐私的保护
TenInt[5] 专注个性化推荐; 结合用户
的社会信息和时间背景
极弱的上下文数据;
无法解释的推荐
TAP-F[6] 克服登记数据稀疏的问题;
捕获由于时间影响用户偏
好的改变
有限的上下文信息;
假设性太强
字段 描述
checkinsCount 所有存在的check-in数据
usersCount 此处已check-in的所有用户
tips 这里的提示数量
likes 喜欢这个兴趣点的用户数量
rating 兴趣点数值评级(0-10)
photos 这个兴趣点的照片数量
price 价格从1(最低价)- 4(最昂贵)
veri?ed 布尔值, 表示该业务的所有者是否已经声明
并验证了这些信息
createdAt 创建兴趣点的时间戳
beenHere 用户到此处的次数
[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.
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
[1] Gong Lijuan,Wang Hao,Zhang Zixuan,Zhu Liping. Reducing Dimensions of Custom Declaration Texts with Word2Vec[J]. 数据分析与知识发现, 2020, 4(2/3): 89-100.
[2] Bengong Yu,Yumeng Cao,Yangnan Chen,Ying Yang. Classification of Short Texts Based on nLD-SVM-RF Model[J]. 数据分析与知识发现, 2020, 4(1): 111-120.
[3] Gang Li,Huayang Zhou,Jin Mao,Sijing Chen. Classifying Social Media Users with Machine Learning[J]. 数据分析与知识发现, 2019, 3(8): 1-9.
[4] 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.
[5] Yong Ding,Lu Cheng,Cuiqing Jiang. Choosing Portfolios Based on Bipartite Graph of P2P Lending Networks[J]. 数据分析与知识发现, 2019, 3(12): 76-83.
[6] 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.
[7] Zhao Yang,Li Qiqi,Chen Yuhan,Cao Wenhang. 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.
[8] Tian Shihai,Lyu Deli. 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn