1. 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

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

3. 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

4. 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

5. 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

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

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

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

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

10. 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

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

12. 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

13. 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

14. 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

15. 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

16. 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