Using Genre Rating Information for Similarity Estimation in Collaborative Filtering (협력필터링에서 유사도 측정을 위한 장르 평가 정보 활용)

Soojung Lee (이수정)

doi:10.9708/jksci.2019.24.12.093

Using Genre Rating Information for Similarity Estimation in Collaborative Filtering

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2019, 24(12), pp.93-100
DOI : 10.9708/jksci.2019.24.12.093
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : September 17, 2019
Accepted : November 23, 2019
Published : December 31, 2019

Soojung Lee ¹

¹경인교육대학교

Accredited

ABSTRACT

Similarity computation is very crucial to performance of memory-based collaborative filtering systems. These systems make use of user ratings to recommend products to customers in online commercial sites. For better recommendation, most similar users to the active user need to be selected for their references. There have been numerous similarity measures developed in literature, most of which suffer from data sparsity or cold start problems. This paper intends to extract preference information as much as possible from user ratings to compute more reliable similarity even in a sparse data condition, as compared to previous similarity measures. We propose a new similarity measure which relies not only on user ratings but also on movie genre information provided by the dataset. Performance experiments of the proposed measure and previous relevant measures are conducted to investigate their performance. As a result, it is found that the proposed measure yields better or comparable achievements in terms of major performance metrics.

KEYWORDS

Collaborative Filtering, Recommender System, Similarity Measure, Data Sparsity Problem, Cold-start problem

Citation status

* References for papers published after 2023 are currently being built.

[confproc] X. Su / 2009 / A Survey of Collaborative Filtering Techniques / Advances in Artificial Intelligence

[journal] S. Du / 2019 / To Make the Travel Healthier : A New Tourism Personalized Route Recommendation Algorithm / Journal of Ambient Intelligence and Humanized Computing 10(9) : 3551~3562

[confproc] J. Gupta / 2015 / Performance Analysis of Recommendation System based on Collaborative Filtering and Demographics / International Conference on Communication Information &Computing Technology : 1~6

[journal] M. Aamir / 2015 / Recommendation System : State of the Art Approach / International Journal Computer Applications 120(12) : 25~32

[journal] M. Jalili / 2018 / Evaluating Collaborative Filtering Recommender Algorithms : A Survey / IEEE Access 6 : 74003~74024

[journal] K. G. Saranya / 2016 / Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique / Indian Journal of Science and Technology 9(29)

[journal] Z. Y. Hafshejani / 2018 / Improving Sparsity and New User Problems in Collaborative Filtering by Clustering the Personality Factors / Electronic Commerce Research 18(4) : 813~836

[journal] Koohi / 2017 / A New Method to Find Neighbor Users that Improves the Performance of Collaborative Filtering / Expert Systems With Applications 83 : 30~39

[journal] B. Zhu / 2018 / An Efficient Recommender System Method based on the Numerical Relevances and the Non-numerical Structures of the Ratings / IEEE Access 6 : 49935~49954

[confproc] M. Li / 2015 / A Collaborative Filtering Algorithm Combined with User Habits for Rating / International Conference on Logistics Engineering, Management and Computer Science : 1279~1282

[journal] W. Wang / 2015 / Collaborative Filtering with Entropy-driven User Similarity in Recommender Systems / International Journal of Intelligent Systems 30(8) : 854~870

[journal] S. Lee / 2019 / Using Entropy for Similarity Measures in Collaborative Filtering / Journal of Ambient Intelligence and Humanized Computing

[confproc] G. Koutrica / 2009 / FlexRecs : Expressing and Combining Flexible Recommendations / Proc. of the ACM SIGMOD Int'l Conf. on Management of Data : 745~758

[journal] J. Bobadilla / 2010 / A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems / Knowledge Based Systems 23(6) : 520~528

[journal] S. Lee / 2017 / Improving Jaccard Index for Measuring Similarity in Collaborative Filtering / Lecture Notes in Electrical Engineering 424 : 799~806

[journal] M. Salehi / 2013 / Attribute-based Collaborative Filtering using Genetic Algorithm and Weighted C-means Algorithm / International Journal of Business Information Systems 13(3) : 265~283

[journal] F. Cacheda / 2011 / Comparison of Collaborative Filtering Algorithms : Limitations of Current Techniques and Proposals for Scalable, High-performance Recommender Systems / ACM Transactions on the Web 5(1) : 1~33

KJCKorea
Journal Central

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Using Genre Rating Information for Similarity Estimation in Collaborative Filtering

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Using Genre Rating Information for Similarity Estimation in Collaborative Filtering

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (1)

REFERENCES (17) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.