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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (3): 9-21    DOI: 10.11925/infotech.2096-3467.2017.1123
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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)
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[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.

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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;
Geo-Social Network[2] 利用指定的地理位置; 对未
MGM[3] 模拟用户check-in行为的地
理影响; 结合用户的社会信
CIAP[4] 利用来自各个设备的上下
好; 结合普遍偏好和个体
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
TAP-F[6] 克服登记数据稀疏的问题;
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
createdAt 创建兴趣点的时间戳
beenHere 用户到此处的次数
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