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Collaborative Tagging in Recommender Systems AE-TTIE JI 1 , CHEOL YEON 1 , HEUNG-NAM KIM 1 , AND GEUN-SIK JO 2. 1 Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University { aerry13 , entireboy , nami }@eslab.inha.ac.kr

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    1. Collaborative Tagging in Recommender SystemsAE-TTIE JI1, CHEOL YEON1, HEUNG-NAM KIM1, AND GEUN-SIK JO2 1 Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University {aerry13, entireboy, nami}@eslab.inha.ac.kr 2School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea 402-751 gsjo@inha.ac.kr

    2. Introduction Recommender System with Collaborative Tagging Experimental Results Conclusions Future Works Introduction Recommender System with Collaborative Tagging Experimental Results Conclusions Future Works Introduction Recommender System with Collaborative Tagging Experimental Results Conclusions Future Works Introduction Recommender System with Collaborative Tagging Experimental Results Conclusions Future Works Introduction Recommender System with Collaborative Tagging Experimental Results Conclusions Future Works Introduction Recommender System with Collaborative Tagging Experimental Results Conclusions Future Works

    3. Introduction

    4. Introduction Collaborative Filtering (CF) Cold-start User Problem Sparsity Problem

    5. Introduction Collaborative Tagging (CT)

    6. Introduction Motivation

    7. Recommender System with CT System Architecture • Part 1: Catching an user’s latent preference! • Candidate Tag Set Generation via CF • Part 2: Probabilistic Recommendation! • Naïve Bayesian Approach

    8. Recommender System with CT Matrices Representing Preferences • User-item binary matrix, R (r × n) • Ru,i : whether a user ur prefers an item in or not. • User-tag matrix, A (r × m) • Au,t : frequency of a tag tm tagged by a user ur. • Tag-item matrix, Q (m × n) • Qt,i : frequency of a tag tm for an item in.

    9. Recommender System with CT Recommendation Process • Candidate Tag Set (CTS) Generation via CF • User-User Similarity • Tag Preference

    10. Recommender System with CT CTS Generation via CF • CTS (Candidate Tag Set) • The latent preference of a target user • User-user Similarity • To find k nearest neighbors (KNN) of a target user based on user-tag matrix A • Tag Preference

    11. Improving limitations of CF via CT Case for Data Sparsity

    12. Improving limitations of CF via CT Case for Data Sparsity

    13. Improving limitations of CF via CT Case for Data Sparsity

    14. Improving limitations of CF via CT Case for Cold-start User

    15. Recommender System with CT Recommendation Process • Item Recommendation • Naïve Bayes Classifier • Top-N Items Recommendation

    16. Recommender System with CT Item Recommendation • Naïve Bayes Classifier • Posterior probability : a preference probability of user u for an item iy with CTSw(u) • Prior probability • Item-conditional Tag Distribution • Top-N Recommendation • TopNu items with the highest Pu,y , |TopNu| ≤ N and TopNu ∩ Iu = Ø

    17. Experimental Results Dataset & Evaluation Metric • Dataset • http://del.icio.us (a social bookmarking service) • Training data : 21,653 / Testing data : 5,413 • Sparsity level of user-item matrix : 0.9989 • Evaluation metric

    18. Experimental Results Benchmark Algorithms • User-based Collaborative Filtering (Badrul Sarwar, and et al., 2000) • Item-based Collaborative Filtering (Mukund Deshpande, and et al., 2004) • KNN size was set to 50 where the performance increase rates were diminished for main comparison. Recommendation size N = 10

    19. Experimental Results Experiments with CTS size • The size of CTS, w, can be a significant factor affecting the quality of recommendation. • w was set to 70, which obtained the best quality for main comparisons. Neighbor size k = 50 Recommendation size N = 10

    20. Experimental Results Comparisons of Overall Performance • Sparsity of the collected dataset affected the performances of all three methods. • Even though the number of recommended items were small, our method outperformed the other two methods. Neighbor size k = 50 CTS size w = 70

    21. Experimental Results Comparisons for Cold-start User • For cold-start users who do not have enough preference information, our method outperformed the other two methods. Neighbor size k = 50 CTS size w = 70 Recommendation size N = 10

    22. Conclusion • We analyzed the potential of collaborative tagging system for applying to recommendation. • User-created tags imply users’ preferences about items as well as metadata about them. • Using tags can partially improve data sparsity and cold-start user problem which are serious limitations of CF recommendation. • Also proposed is a novel recommender system based on collaborative tags of users using CF scheme. • Our algorithm obtained better recommendation quality compared to traditional CF schemes. • It provided more suitable items for user preferences even though the number of recommended items were small.

    23. Future Work • “Noise” tags can be included in CTS. • Some tags are too personalized or content-criticizable (e.g., bad, myWork, to read etc.) • They should be treated for more personalized and valuable analysis. • There are common issues of keyword-based analysis. • Polysemy, synonymy and basic level variation. • Semantic tagging is an interesting approach to address these issues.