Browse > Article
http://dx.doi.org/10.7472/jksii.2016.17.6.133

Tag Based Web Resource Recommendation System  

Song, Je-In (Dept. of Software, Gachon Univ.)
Jeong, Ok-Ran (Dept. of Software, Gachon Univ.)
Publication Information
Journal of Internet Computing and Services / v.17, no.6, 2016 , pp. 133-141 More about this Journal
Abstract
Recent web services provide tagging function to users, and let them express the topic of the contents of their articles. Moreover, we can extract context information like emotion of the writer efficiently by using tags attached to the articles or images. And we are able to better understand article than traditional algorithm. (eg. TF-IDF) Therefore, if we use tags in recommendation system, we can recommend high quality resources to the users. This study proposes a recommendation method that provide web resources (articles, users) through simple algorithm based on related tag set extracted from the article. Through the experiments, we show that the result was satisfactory, and we measure the satisfaction of users.
Keywords
Recommendation; Tagging; Context Information; Web service; Web resource;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Recommendation System, http://rosaec.snu.ac.kr/meet/file/20120728b.pdf
2 Zellig S. Harris, "Distributional Structure", WORD, Vol. 10:2-3, pp.146-162, 2015.
3 Tomas Milolov, "Distributed Representations of Words and Phrases and their Compositionality" Advanced in Neural Information Processing Systems 26, 2013.
4 Archifeeld, http://feeeld.com
5 Jo Hyeon, "A recommendation algorithm which reflects tag and time information of social network", Journal of Korean Society for Internet Information, v.14, no.2, pp.15-24, 2013
6 Borkur Sigurbjornsson, "Flickr Tag Recommendation based on Collective Knowledge", pp.327-336, WWW, 2008
7 Shilad Sen, "Tagommenders: Connecting Users to Items through Tags", pp. 671-680, WWW, 2009
8 Sogol Naseri, "Enhancing tag-based collaborative filtering via integrated social networking information", pp. 760-764, ASONAM '13, 2013
9 Frederico Durao, "A Personalized Tag-Based Recommendation in Social Web Systems", pp. 40-49, Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, 2009
10 Rakesh Agrawal, "Fast Algorithm for Mining Association Rules"
11 JIAWEI HAN, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, 8, pp. 53-87, 2004   DOI
12 Google Analytics, https://www.google.com/analytics