1. ItemBased Collaborative Filtering Recommendation Algorithms 1

2. Introduction What are Recommender systems? K-nearest neighbour collaborative filtering based Key challenges Comparison between old ones and new ones 2

3. Collaborative Filtering What is it used for? Challenges Types Comparison between Item-based and User-based 3

4. Collaborative Filtering Based Recommender System Approaches Types Memory-based Collaborative Filtering Algorithms Model-based Collaborative Filtering Algorithms 4

5. User-based Collaborative Filtering Algorithm Successful in past Challenges Sparsity Scalability 5

6. Item-based Collaborative Filtering Algorithms Item Similarity Computation Prediction Computation Performance Implication 6

7. Item Similarity Computation Cosine-based Similarity Correlation-based Similarity Adjusted Cosine Similarity 7

8. Cosine-based Similarity 8

9. Prediction Computation Weighted Sum Regression 9

10. Weighted Sum 10

11. Experimental Evaluation Data Set Evaluation Metrics Statistical accuracy metrics Decision support accuracy metrics 11

12. The paper… Implemented three different similarity algorithms basic cosine, adjusted cosine and correlation. For each similarity algorithm it implemented the algorithm to compute the neighborhood and used weighted sum algorithm to generate the prediction. The experiment is ran on training data and used test set. To determine the sensitivity of density of the data set, they carried out an experiment where the value of x was varied from 0.2 to 0.9 in an increment of 0.1. They also varied the number of neighbors to determine the sensitivity of this parameter because the size of the neighborhood has significant impact on the prediction quality. 12

13. Result and Conclusion The item-based algorithm provides better quality of prediction than the user-based algorithm. The paper represented and experimentally evaluated a new algorithm for CF-based recommender systems. The result shows that item-based techniques hold the promise of allowing CF-based algorithms to scale to large data sets and at the same time produce high-quality recommendation. 13

14. Thanks… 14