Collaborative Tagging in Recommender Systems AE-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 2 School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea 402-751 gsjo@inha.ac.kr

Recommender System with Collaborative Tagging 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

Introduction

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

Collaborative Tagging (CT) Introduction Collaborative Tagging (CT)

Introduction Motivation

System Architecture Part 1: Catching an user’s latent preference! 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

Matrices Representing Preferences 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.

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

CTS Generation via CF CTS (Candidate Tag Set) User-user Similarity 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

Improving limitations of CF via CT Case for Data Sparsity

Improving limitations of CF via CT Case for Data Sparsity

Improving limitations of CF via CT Case for Data Sparsity

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

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

Item Recommendation Naïve Bayes Classifier Top-N Recommendation 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

Dataset & Evaluation Metric 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 users Items tags book markings taggings 1,544 17,390 10,077 27,066 44,681

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

Experiments with CTS size 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

Comparisons of Overall Performance 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

Comparisons for Cold-start User 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

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.

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.