Please wait a minute...
Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (2): 21-32    DOI: 10.11925/infotech.2096-3467.2018.0497
Current Issue | Archive | Adv Search |
Recommending Personalized Contents from Cross-Domain Resources Based on Tags
Jiaxin Ye,Huixiang Xiong()
School of Information Management, Central China Normal University, Wuhan 430079, China
Download: PDF(536 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study tries to generate personalized contents from cross-domain resources based on the relationship among online tags. [Methods] First, we proposed a cross-domain resource recommendation model. Then, we identified tags appropriate for cross-domain recommendations. Third, we combined the DBSCAN algorithm with the tag vector to obtain the initial recommendation candidates. Finally, we used the TF-IDF algorithm along with the personalized tags to improve the initial list. [Results] The recall, precision, and F-measure of the resource-based recommendation method were 0.82, 0.75, and 0.78. The recall, precision, and F-measure of the user tag based recommendation method were 0.80, 0.74, and 0.77. Our results were strongly correlated to users’ interests. [Limitations] The number of tags for the initial recommendation candidates was small, which could not fully represent the resources. It is difficult to collect tags for the second round recommendation. [Conclusions] Once tags from different domains are related to each other, we can use them to recommend contents from cross-domain resources.

Key wordsTag      Cross-Domain      Resource Clustering      Personalized Recommendation     
Received: 03 May 2018      Published: 27 March 2019

Cite this article:

Jiaxin Ye,Huixiang Xiong. Recommending Personalized Contents from Cross-Domain Resources Based on Tags. Data Analysis and Knowledge Discovery, 2019, 3(2): 21-32.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0497     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I2/21

[1] Bertram R, Schrimpf G, Stamm-Wilbrandt H. System and Method for Item Recommendations: USA, US8700448B2[P].2014-04-15.
[2] 熊回香, 杨雪萍. 社会化标注系统中的个性化信息推荐研究[J]. 情报学报, 2016, 35(5): 549-560.
[2] (Xiong Huixiang, Yang Xueping.Personalized Information Recommendation Research Based on Combined Condition in Folksonomies[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(5): 549-560.)
[3] 李枫林, 陈德鑫, 梁少星. 基于语义关联和情景感知的个性化推荐方法研究[J]. 情报杂志, 2015, 34(10): 189-195.
[3] (Li Fenglin, Chen Dexin, Liang Shaoxing.Research on Personalized Recommendation Method Based on Semantic Association and Context Awareness[J]. Journal of Intelligence, 2015, 34(10): 189-195.)
[4] Hicken W, Holm F, Clune J, et al. Music Recommendation System and Method: USA, US20050038819A1[P].2005-02-17.
[5] 张秀伟, 何克清, 王健, 等. Web 服务个性化推荐研究综述[J]. 计算机工程与科学, 2013, 35(9): 132-140.
[5] (Zhang Xiuwei, He Keqing, Wang Jian, et al.A Survey of Personalized Web Service Recommendation[J]. Computer Engineering & Science, 2013, 35(9): 132-140.)
[6] 刘静, 熊才平, 丁继红, 等. 教育信息资源个性化推荐服务模式研究[J]. 中国远程教育, 2016 (2): 5-9, 79.
[6] (Liu Jing, Xiong Caiping, Ding Jihong, et al.A Personalized Approach to Recommending Educational Information Resources[J]. Distance Education in China, 2016(2): 5-9, 79.)
[7] Ebadi A, Krzyzak A.A Hybrid Multi-Criteria Hotel Recommender System Using Explicit and Implicit Feedbacks[J]. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2016, 10(8): 1450-1458.
[8] 房小可, 纪春光. 基于标签主题和概念空间的个性化推荐研究[J]. 情报理论与实践, 2015, 38(5): 105-111.
[8] (Fang Xiaoke, Ji Chunguang.Research on the Personalized Recommendation Based on Tag Topic and Concept Space[J]. Information Studies: Theory & Application, 2015, 38(5): 105-111.)
[9] Vairavasundaram S, Varadharajan V, Vairavasundaram I, et al.Data Mining-Based Tag Recommendation System: An Overview[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2015, 5(3): 87-112.
[10] Zhang Z K, Zhou T, Zhang Y C.Personalized Recommendation via Integrated Diffusion on User-Item-Tag Tripartite Graphs[J]. Physica A: Statistical Mechanics and Its Applications, 2010, 389(1): 179-186.
[11] Li D, Xu Z, Yang M, et al.Item Recommendation in Social Tagging Systems Using Tag Network[J]. Journal of Information & Computational Science, 2013, 10(13): 4057-4066.
[12] 于洪, 李俊华. 结合社交与标签信息的协同过滤推荐算法[J]. 小型微型计算机系统, 2013, 34(11): 2467-2471.
[12] (Yu Hong, Li Junhua.Collaborative Filtering Recommendation Algorithm Using Social and Tag Information[J]. Journal of Chinese Computer Systems, 2013, 34(11): 2467-2471.)
[13] Ji A T, Yeon C, Kim H, et al.Collaborative Tagging in Recommender Systems[C]// Proceedings of the 20th Australian Joint Conference on Artificial Intelligence. Springer, 2007: 377-386.
[14] 廖志芳, 王超群, 李小庆, 等. 张量分解的标签推荐及新用户标签推荐算法[J]. 小型微型计算机系统, 2013, 34(11): 2472-2476.
[14] (Liao Zhifang, Wang Chaoqun, Li Xiaoqing, et al.Tag Recommendation and New User Tag Recommendation Algorithms Based on Tensor Decomposition[J]. Journal of Chinese Computer Systems, 2013, 34(11): 2472-2476.)
[15] Ifada N, Nayak R.Tensor-Based Item Recommendation Using Probabilistic Ranking in Social Tagging Systems[C]// Proceedings of the 23rd International Conference on World Wide Web. ACM, 2014: 805-810.
[16] Niwa S, Doi T, Honiden S. Web Page Recommender System Based on Folksonomy Mining for ITNG’06 Submissions[C]// Proceedings of the 3rd International Conference on Information Technology: New Generations. IEEE, 2006: 388-393.
[17] Gemmell J, Shepitsen A, Mobasher B, et al.Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering[C]// Proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery. Springer, 2008: 196-205.
[18] 杨丹, 曹俊. 基于Web2. 0的社会性标签推荐系统[J]. 重庆工学院学报: 自然科学版, 2008, 22(7): 51-55.
[18] (Yang Dan, Cao Jun.Web Page Recommender System Based on Social Tags in Web 2.0[J]. Journal of Chongqing Institute of Technology: Natural Science, 2008, 22(7): 51-55.)
[19] Mao J, Lu K, Li G, et al.Profiling Users with Tag Networks in Diffusion-Based Personalized Recommendation[J]. Journal of Information Science, 2016, 42(5): 711-722.
[20] Liu H.Resource Recommendation via User Tagging Behavior Analysis[J]. Cluster Computing, 2017. DOI: 10.1007/s10586- 017-1459-2.
[21] 欧辉思, 曹健. 面向跨领域的推荐系统研究现状与趋势[J]. 小型微型计算机系统, 2016, 37(7): 1411-1416.
[21] (Ou Huisi, Cao Jian.Survey on Research and Progress of Cross-Domain Recommendation[J]. Journal of Chinese Computer Systems, 2016, 37(7): 1411-1416.)
[22] 易明, 操玉杰, 沈劲枝, 等. 社会化标签系统中基于密度聚类的Web 用户兴趣建模方法[J]. 情报学报, 2011, 30(1): 37-43.
[22] (Yi Ming, Cao Yujie, Shen Jinzhi, et al.An Approach to Web User Interest Modeling Based on Density-based Clustering Algorithm in the Social Tag System[J]. Journal of the China Society for Scientific and Technical Information, 2011, 30(1): 37-43.)
[23] 石陆魁, 何丕廉. 一种基于密度的高效聚类算法[J]. 计算机应用, 2005, 25(8): 1824-1826.
[23] (Shi Lukui, He Pilian.Efficient Density-based Clustering Algorithm[J]. Computer Applications, 2005, 25(8): 1824-1826.)
[24] 李双庆, 慕升弟. 一种改进的DBSCAN算法及其应用[J]. 计算机工程与应用, 2014, 50(8): 72-76.
[24] (Li Shuangqing, Mu Shengdi.Improved DBSCAN Algorithm and Its Application[J]. Computer Engineering and Applications, 2014, 50(8): 72-76.)
[1] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[2] Yiwen Zhang,Chenkun Zhang,Anju Yang,Chengrui Ji,Lihua Yue. A Conditional Walk Quadripartite Graph Based Personalized Recommendation Algorithm[J]. 数据分析与知识发现, 2019, 3(4): 117-125.
[3] Yue Yuan,Dongbo Wang,Shuiqing Huang,Bin Li. The Comparative Study of Different Tagging Sets on Entity Extraction of Classical Books[J]. 数据分析与知识发现, 2019, 3(3): 57-65.
[4] Xiangdong Li,Fan Gao,Youhai Li. Categorizing Documents Automatically within Common Semantic Space[J]. 数据分析与知识发现, 2018, 2(9): 66-73.
[5] Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[6] Guanghui Ye,Jinglan Hu,Jian Xu,Lixin Xia. Analyzing Growth Trends and Attachment Mode of Social Blog Tags[J]. 数据分析与知识发现, 2018, 2(6): 70-78.
[7] Wei Lu,Mengqi Luo,Heng Ding,Xin Li. Image Annotation Tags by Deep Learning and Real Users: A Comparative Study[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[8] Huixiang Xiong,Jiaxin Ye,Wuxuan Jiang. Clustering Social Tags with Improved DBSCAN Algorithm[J]. 数据分析与知识发现, 2018, 2(12): 77-88.
[9] Haili Tu,Xiaobo Tang. Building Product Recommendation Model Based on Tags[J]. 数据分析与知识发现, 2017, 1(9): 28-39.
[10] Huixiang Xiong,Wuxuan Jiang. Clustering and Recommending Users Based on Tags and Relation Network[J]. 数据分析与知识发现, 2017, 1(6): 36-46.
[11] Meimei Chen,Kangjie Xue. Personalized Recommendation Algorithm of Multi-faceted Trust Tensor Based on Tag Clustering[J]. 数据分析与知识发现, 2017, 1(5): 94-101.
[12] Meimei Chen, Kangjie Xue. Personalized Recommendation Algorithm Based on Modified Tensor Decomposition Model[J]. 数据分析与知识发现, 2017, 1(3): 38-45.
[13] Mengyao Xie,Xuwei Pan. Constructing Dynamic Social Tag Cloud for User Interests[J]. 数据分析与知识发现, 2017, 1(2): 35-40.
[14] Bo Guo,Shouguang Li,Hao Wang,Xiaojun Zhang,Wei Gong,Zhaojun Yu,Yu Sun. Examining Product Reviews with Sentiment Analysis and Opinion Mining[J]. 数据分析与知识发现, 2017, 1(12): 1-9.
[15] Tan Xueqing,Zhang Lei,Huang Cuicui,Luo Lin. A Collaborative Filtering and Recommendation Algorithm Using Trust of Domain-Experts and Similarity[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn