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

2. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Introduction Increasing variety of content in mobile web environment –Music –Graphics –Games –Other mobile content Searching for the music on mobile 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 1

3. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 2

4. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Proposed system CoFoSIM –COllaborative Filtering with Ordinal Scale-based IMplicit ratings –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 3

5. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Scenario of searching for music 4

6. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 5 Methodology:

7. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 6 Methodology:

8. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 7 Methodology:

9. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Step 2-2. optimal preference intensity matrix creation –An optimal preference intensity matrix X –DEF) the optimal preference intensity matrix : preference intensity matrix ☞ 8 Methodology: Phase 2 : Ordinal scale-based customer profile creation ^

10. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Step 2-3. Ordinal scale-based customer profile creation –Creating the ordinal scale-based customer profile for recommender system Requires a series of transformations(optimal preference intensity matrix) –Sorted as 9 Methodology: Phase 2 : Ordinal scale-based customer profile creation

11. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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) R AB =0.63, R AC =0.30, R AD =0.81, R AE =0.70, R AF =0.43 10 Methodology: Phase 3 : neighborhood formation and recommendation generation

12. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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. 11 Methodology: Phase 3 : neighborhood formation and recommendation generation

13. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 12

14. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 13 Experimental results:

15. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 14 Experimental results:

16. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 15 Experimental results:

17. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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. 16 Conclusion

18. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Discussion CF-based recommender + LBS Drawbacks 분석방법 –T-test –ANOVA –MAE(mean absolute error) 17