Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

1. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations S.-K. Lee et al., KAIST, Information Sciences, Vol. 180, Issue 11, pp. 2142-2155, 2010. 이시혁 theshy@sclab.yonsei.ac.kr

2. Introduction • Increasing variety of content in mobile web environment • Music • Graphics • Games • Other mobile content • Searching for the music onmobile web devices • Inefficiencies of searching sequentially • Log on to a site to download music : best selling or newest music • Pages through the list and selects items to inspect • Customer : buy or repeats the steps • Compared to PCs • Smaller screens • Fewer input keys • Less sophisticated browsers

3. Recommender system • Collaborative filtering(CF) • One of the variety of recommendation techniques • Identify customers : similar to target customer and recommend items(customers have liked) • CF based recommender systems • Customer profile : identify preferences and make recommendations • Explicit ratings • Well-understood and fairly precise, but some problems in mobile domain • User interface of mobile devices is typically poor • The cost of using the mobile web through these devices is high • Implicit ratings • Used cardinal scales to increase the accuracy of estimation • Uncertain whether cardinal scales are also better in implicit ratings

4. Proposed system • CoFoSIM • COllaborative Filtering with Ordinal Scale-based IMplicitratings • CF recommendation methodology for the mobile music • mWUM • Mobile Web Usage Mining • Capture implicit preference information • Apply data mining techniques to discover customer behavior patterns by using mobile web log data • All recorded transactions in mobile web logs are individually analyzed

5. Scenario of searching for music

6. Methodology: General behavior pattern in the mobile web • General behavior patterns in the mobile web enviornment • Ignore : not clicking on the title • Click-through : clicking on a certain title, viewing the detail information • Pre-listen : a sample of the music • Purchase : buying the music(clicked-through or pre-listened) • Preference order • {music ignored(never clicked)} < {music clicked-through} < {music pre-listened} < {music purchased} • Greatest weight : purchased music

7. Methodology: Phase 1 : mobile web usage mining(mWUM) • Creating customer action • Step1-1. data preprocessing • including data cleaning, user identification, session identification • Most web pages contain numerous irrelevant items(gif, jpg, swf…) • Creating customer’s session file • Step 1-2. customer behavior mining • Creating specific matrix : the customer actions set • The customer action set C : matrix • Containing the numerical weights of the target customer’s shopping behaviors for music items

8. Methodology: Phase 2 : Ordinal scale-based customer profile creation • Customer’s product interests or preferences : the customer profile • Requires three sequential steps • Step2-1. preference intensity matrix creation • Customer action set for each customer : L rows • Limits on the number of music items(they are capable of browsing through) • Individual rows of customer action sets contain a part of the preferences information • DEF) The preference intensity matrix if matrix for which

9. Methodology: Phase 2 : Ordinal scale-based customer profile creation • Step 2-2. optimal preference intensity matrix creation • An optimal preference intensity matrix X • DEF) the optimal preference intensity matrix : preference intensity matrix ☞ ^

10. Methodology: Phase 2 : Ordinal scale-based customer profile creation • Step 2-3. Ordinal scale-based customer profile creation • Creating the ordinal scale-based customer profile for recommender systemRequires a series of transformations(optimal preference intensity matrix) • Sorted as

11. Methodology: Phase 3 : neighborhood formation and recommendation generation • Given the customer profile • Perform - the CF-based recommendation procedure for a target customer • Step 3-1. neighborhood formation • Computes the similarity between customers and forms • A neighborhood between a target customer and a group of like-minded customers • Similarity : between the target customer a and other customers b • Example 4) RAB=0.63, RAC=0.30, RAD=0.81, RAE=0.70, RAF=0.43

12. Methodology: Phase 3 : neighborhood formation and recommendation generation • Step 3-2. recommendation generation • Top-N recommendation • Recommendation list of N music items • Previously purchased music items : excluded, each customer’s purchase patterns or coverage • Music j, target customer a • Exam6) result in exam5.

13. Experimental environment • Experiment design • Live user experiment • Benchmark system • CoFoSIM running PC (same interface- mobile) • cardinal scale-based recommender system (CS-RS) • ordinal scale-based recommender system (OS-RS) • Common factor for systems • Fixed neighborhood size : 10 • Recommendation lists(Top-n) : 9 • Data collection • Between May 1 and June 18, 2007 • 317 real mobile Web customers • Previous experience purchasing music from real mobile Web sites

14. Experimental results: Variation of error by rating scales • Compared the accuracy of CS-RS and OS-RS • OS-RS average : 0.6677, higher 29% than CS-RS (during 7-weeks) • T-test(OS-RS, CS-RS) : -4.309(d.f=138, p<0.01) • Different mean of the correlation metric between the two systems • OS-RS : smaller estimation error than CS-RS

15. Experimental results: Variation of estimation error by consensus models • Compared CoFoSIM with OS-RS (Used the ordinal scale) • CoFoSIM 11% higher than OS-RS • T-test (OS-RS, CoFoSIM) : -2.822(d.f=138, p<0.01) • Different mean of the correlation metric between the two systems • CoFoSIM: smaller estimation error than OS-RS

16. Experimental results: relationship between the estimation error and recommendation quality • Performance (precision, recall, and F1) • OS-RS > CS-RS : 60%, 15%, and 44% • CoFoSIM > OS-RS : 16%, 12%, and 15% • T-test • OS-RS and CoFoSIM- differences • t={3.96, 16.25, and 5.43} • One-way ANOVA test (p<0.01) • F(precision)=32.2 • F(recall)=9.5 • F(F1)=17.9

17. Conclusion • CoFoSIM • viable CF-based recommender system for the mobile web • Enhance the quality of recommendations while mitigating customers’ burden of explicit ratings • Customers will be able to purchase content with much lower connection time on the mobile Web because they will be able to easily find the desired items • mobile content providers will be able to improve their profitability and revenues because their purchase conversion rate will be improved through increased customer satisfaction.

18. Discussion • CF-based recommender + LBS • Drawbacks • 분석방법 • T-test • ANOVA • MAE(mean absolute error)