1. NRCF: A Novel Collaborative Filtering Method for Service Recommendation
Huifeng Sun 1, Zibin Zheng 2, Junliang Chen 1, Michael R. Lyu 2
1, Beijing University of Posts and Telecommunications
2, The Chinese University of Hong Kong
July 4, 2011

2. What’s Collaborative Filtering (CF)
The basic idea of CF methods is: predict the utility of items for a particular user based on the items previously rated by other users.

3. Research Question & Its Meaning for Our Paper
Question definition
There are many (several tens of thousand) web services and lots of users on the Internet, how to predict QoS value (e.g. RTT and failure-rate) for users and services?
Meaning
If we predict QoS value (e.g. RTT) properly
it assists users make decision when selecting optimal service from a set of functionality-equivalent web services
it helps discover good performing Web services for the current user, and recommend potential users to service providers

4. Our Contribution a new similarity measure
It take characteristics of Web service QoS into consideration and can achieve more accurate QoS value prediction results
a novel collaborative filtering approach
It significantly improves the recommendation performance comparing with the other well-known approaches

5. Motivation for the new similarity measure
Tranditional smilarity meausres’ shortcoming:
Pearson Correlation Coefficient (PCC):
PCC overlooks dimension-number difference of vectors in different vector spaces
Cosine-based approach (COS)
COS neglects length difference of different vectors.

6. The new similarity measure
To overcome the shortcomings ,we propose a simialrity measure named normal recovery (NR) as follow
Our NR approach first normalized the user QoS values to the same range, then unify similarity of the scaled user vectors (or item vectors) in different multidimensional vector spaces.

7. The novel collaborative filtering approach
Based on our NR similarity measure approach, we propose an innovative memory-based CF method, named normal recovery collaborative filtering (NRCF) as follow

8. NRCF for Web Service Recommendation
In Web service recommendation, the user QoS styles and the item QoS styles are very different from each other
since service users and Web services are located in many countries that are far from each other,
and the network through which Web services are invoked is highly dynamic
Our NRCF approach can adapt to different environtment easily
since it considers the QoS style difference and makes user of information of both similar users and similar items for making prediction.

9. Experiment
The experiment is conducted on a Web service QoS dataset collected by ourselves
Experimental setup: 5-fold cross validation
Evaluation metric: Mean Absolute Error (MAE).

10. Experimental Results NR’s performance
Compare 3 similarity measurement: PCC, COS, our NR
NR approach significantly improves the prediction accuracy (24.45% and 32.80% better than PCC and COS, respectively).

11. Experimental Results cont.
2. NRCF’s performance
Compare the following 6 approaches: User-Mean (UMEAN), Item-Mean (IMEAN), User-based CF adopting PCC (UPCC), Item-based CF adopting PCC (IPCC), WSRec, Our NRCF.
Our NRCF significantly outperforms all the competing approaches consistently, with improvement of 27.17% to 39.59% better than the best results of other competing approaches.

12. Thank you!