1. Fusing Rating-based and Hitting-based Algorithms in Recommender Systems
Xin Xin

2. Outline
Motivation
Our Approach
Experiments

3. Recommender System X: Observations
Avg. ratings of the same ages and gender
Avg. ratings of the same genres
trust relation

4. Rating-based Vs Hitting-based Recommendation
Rating-based: whether recommended items can be rated with high scores by the user.
Hitting-based: how many recommended items will be hit by the user in the future.

5. Limitations Item Missing Problem Item improper Problem
hitting-based missed D: cumulate a large number of small effects can obtain great potential
ratting-based missed A: directly reflect sales of companies
Item improper Problem
Hitting-based: #A = #B (ideal: #B>>#A) user suffer from low quality items
Ratting-based:#D > #B (ideal: #B>>#D) user will not interested in visiting the recommended results

6. Motivation: fusing rating-based and hitting-based recommendation
State-of-the-art ratting-based methods
EigenRank (random walk theory)
Model both preference order and ratings
Outperform other methods
State-of-the-art hitting-based methods
Co-occurrence-based methods (relational feature)
Hitting-frequency-based methods (local feature)
Challenge: How to combine them together?
Linear integration: unnatural combine incompatible feature function values
0.003(distribution in EigenRank)*a+8000(hitting freq)*b?
Rank combination: loss quantity information

7. Our contribution
Propose a new combination model by extending random walk in EigenRank from discrete-time Markov Process (DMP) to continuous-time Markov Process (CMP)
by employing queueing theory, the combination has an intuitive interpretation, making the feature functions being naturally combined without losing quantity information
the accuracy is better than other combination methods through experimental results

8. Outline Motivation Our Approach Experiments EigenRank
Co-occurrence Combination
Hitting Frequency Combination
Algorithms
Experiments

9. EigenRank
Stationary distribution:

10. Co-occurrence Combination
frequent co-occurrence items in the past are also likely to appear together in the future.

11. Hitting Frequency Combination
popular items are likely to interest users.

12. Hitting Frequency Formulation
1) costumers’ arrival follows the time-homogenous Poisson Process.
2) service time follows exponential distribution with the same service rate u.
3) waiting time of a customer on the condition that there is a queue:

13. Algorithm Complexity 1) probability transition matrix building;
O(number of users)
2) stationary distribution calculation.
O(number of items)

14. Outline
Motivation
Our Approach
Experiments

15. Experiments Setup Datasets Metric Protocol
MovieLens
Netflix
Metric
Protocol
MovienLens: Given 5, Given 10, Given 15
Netflix: Given 5, Given 10, Given 20

16. Empirical Study of Traditional Methods on Multiple Metrics

17. Impact of Parameters

18. Overall Performance

19. Distribution of Recommended Results

20. More Detailed Results

21. Thank you very much xxin@cse.cuhk.edu.hk