Browse > Article
http://dx.doi.org/10.9708/jksci.2018.23.07.011

How to improve the diversity on collaborative filtering using tags  

Joo, Jin-Hyeon (Dept. of Computer Science, Hoseo University)
Park, Geun-Duk (Dept. of Computer Science, Hoseo University)
Publication Information
Journal of the Korea Society of Computer and Information / v.23, no.7, 2018 , pp. 11-17 More about this Journal
Abstract
In this paper, we propose how to improve the lack of diversity in collaborative filtering, using tag scores contained in items rather than ratings of items. Collaborative filtering has excellent performance among recommendation system, but it is evaluated as lacking diversity. In order to solve this problem, this paper proposes a method for supplementing diversity lacking in collaborative filtering by using tags. By using tags that can be used universally without using the characteristics of specific articles in a recommendation system, The proposed method can be used.
Keywords
Recommendation System; Collaborative Filtering; Tag; Diversity;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Jong-Chan Lee, Moon-Ho Lee. "Big data-based information recommendation system." Journal of the Korea Institute of Information and Communication Engineering, 22.3 pp. 443-450. March 2018.   DOI
2 Eun-Young Bae, Seok-Jong Yu. "Keyword-based Recommender System Dataset Construction and Analysis". The Journal of Korean Institute of Information Technology, 16(6), pp. 91-99. June 2018
3 Seok-Jong Yu. "A Study of Improvement of Individual Item Diversity in Collaborative Filtering-based Recommendation." The Journal of Korean Institute of Information Technology, 14.8 pp. 89-94. October 2016
4 Hyunwoo Kim, Hyoung-Joo Kim. "Item Recommendation using Tag Expansion and Temporal Information." Journal of KIISE : Computing Practices and Letters, 18(7), pp. 521-527. July 2012.
5 Min Sehee, Yeom Hongseon. "Recipe Recommendation System based on Ingredient By using Hybrid Filtering." Journal of the Korean Information Science Association, 1575-1577. December 2014.
6 Han Yong Eom, Chang Hyeon Park. "Recommender System based on Hidden Markov Model." Korea Electronic Engineering Conference, 78-81. November 2001.
7 Nachiketa Sahoo, Param Vir Singh, and Tridas Mukhopadhyay. "A hidden Markov model for collabor ative filtering". MIS Q. 36, 4 1329-1356. December 2012.
8 Jong-Hun Kim, Kyung-Yong Chung, Joong-Kyung Ryu, Kee-Wook Rim, Jung-Hyun Lee. 2008. A Recommendation Agent System Using HMM-Based Collaborative Filtering in Smart Home Environment. 11-13. Nov 2008.
9 N. Lathia, S. Hailes, L. Capra, and X. Amatriain, "Temporal Diversity in Recommendation Systems", SIGIR, Geneva, pp. 210-217, July. 2010.
10 M. Zhang and N. Hurley, "Avoiding Monotony: Improving the diversity of Recommendation Lists", RecSys 2008, Lausanne, Switzerland, pp. 123-130, Oct. 2008.
11 Jieun Son, Seoung Bum Kim, Hyunjoong Kim, Sungzoon Cho. "Review and Analysis of Recommender Systems." Journal of the Korean Institute of Industrial Engineers, 41.2 pp. 185-208, April 2015   DOI
12 O. Celma and P. Herrera, "A New Approach to Evaluating Novel Recommendations", RecSys 2008, Lausanne, Switzerland, pp. 179-186, Oct. 2008.
13 G. Adomavicius and Y. Kwon, "Improving Aggregate Recommendation Diversity Using Ranking-based Techniques", IEEE Transactions on Knowledge and Data Engineering. Vol. 24, No. 5, pp. 896-911, May 2012.   DOI
14 Seok-Jong Yu."Frequency-sensitive Diversification in Collaborative Filtering". Journal of the Korea Information Technology Association, 13(7), 93-98. July 2015.
15 T. Zhou, Z. Kuscsik, J-G. Liu, M. Medo, J. R. Wakeling, and Y-C. Zhang, "Solving the apparent diversity-acc uracy dilemma of recommender systems", Proc. of the National Academy of Sciences of the United States of America 107, 10, pp. 4511-4515, Feb. 2010.   DOI
16 C-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, "Improving recommendation lists through topic diversification", WWW 2005, Chiba, Japan, pp. 22-32, Jan. 2005.