ITEM BASED COLLABORATIVE FILTERING RECOMMENDATION ALGORITHEMS Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Proceedings of the tenth international conference on World Wide Web, 2001 석사 1차 지 애 띠

OVERVIEW Collaborative Filtering Item based Approach Experiment Conclusion 2019년 1월 18일 ESLAB, Inha University

INTRODUCTION Collaborative Filtering Problems of CF Successful in Information Filtering applications & E-commerce applications Problems of CF Scalability Quality of Recommendations Research Contributions of This Paper Analysis of Item based prediction Algorithms & Ways to Implement it. Formulation of pre computed model of Item Similarity to increase online scalability Experimental comparison of quality of Item based algorithms to the User-based algorithms 2019년 1월 18일 ESLAB, Inha University

COLLABORATIVE FILTERING Collaborative Filtering Process List of m Users U = {u1, u2, . . . , um} List of n Items I = {i1, i2, . . . , in} Prediction is a numeral value, Pa.j, expressing the predicted likeliness of item ij ∉ Iua for the active user ua Recommendation is a list of N items Ir ⊂ I, that the active user will like the most 2019년 1월 18일 ESLAB, Inha University

COLLABORATIVE FILTERING Two Categories Memory-based CF - Using the entire user-item database to find of neighbors - Use of statistical techniques - Nearest-neighbor Model-based CF - Probabilistic model of user ratings - Bayesian Network, Clustering Approach Challenges Sparsity Scalability 2019년 1월 18일 ESLAB, Inha University

ITEM-BASED APPROACH Item Similarity Computation 2019년 1월 18일 ESLAB, Inha University

ITEM-BASED APPROACH Cosine-based similarity Correlation-based Similarity Adjusted Cosine Similarity 2019년 1월 18일 ESLAB, Inha University

ITEM-BASED APPROACH Prediction Computation 2019년 1월 18일 ESLAB, Inha University

ITEM-BASED APPROACH Weighted Sum Regression 2019년 1월 18일 ESLAB, Inha University

EXPERIMENT Data set Evaluation Metrics Movie data from MovieLens recommender system Evaluation Metrics Statistical accuracy metrics Evaluate the accuracy of a system by comparing the numerical recommendations scores against actual user ratings MAE (Mean Absolute Error) Root Mean Squared Error (RMSE), Correlation Decision support accuracy metrics Evaluate how effective a prediction engine is helping a user select high quality items from the set of all items Reversal rate, weighted errors. ROC sensitivity 2019년 1월 18일 ESLAB, Inha University

EXPERIMENT Effect of Similarity Algorithms 2019년 1월 18일 ESLAB, Inha University

EXPERIMENT Sensitivity of Training / Test data Quality of prediction increase with increase in Training data x=0.8 used Experiments with neighborhood size Size selected as 30 2019년 1월 18일 ESLAB, Inha University

EXPERIMENT Quality Experiments Performance results Item-based algorithms provide better quality Regression based approach perform better with very sparse dataset Performance results To achieve more scalability Sensitivity of model size Impact of model size on response time and throughput 2019년 1월 18일 ESLAB, Inha University

EXPERIMENT Sensitivity of Model Size Number of similar Item varied from 25 to 200 ‘k’ items used of ‘l’ for the prediction generation process With k = 25 & x = 0.8 the accuracy achieved was up to 98% 2019년 1월 18일 ESLAB, Inha University

EXPERIMENT Impact of the Model size on run time and throughput Small model with appropriate ‘x’ value require less run time To prove it strongly the predictions generated per second It is found that smaller models give more throughput 2019년 1월 18일 ESLAB, Inha University

CONCLUSION Item-Item scheme provides better quality of predictions than the User-User scheme (k-nearest neighbor) Item neighborhood is comparatively static, which results in high performance Due to model based approach it is possible to get good prediction quality with small subset of items 2019년 1월 18일 ESLAB, Inha University