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

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

이시혁

[email protected]

S.-K. Lee et al., KAIST, Information Sciences, Vol. 180, Issue 11, pp. 2142-2155, 2010.

S FT COMPUTING @ YONSEI UNIV . KOREA16

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

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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

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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 pat-

terns by using mobile web log data– All recorded transactions in mobile web logs are individually ana-

lyzed

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Scenario of searching for music

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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 informa-

tion– 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

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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

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Methodology:


Phase 2 : Ordinal scale-based customer profile creation

• Customer’s product interests or preferences : the customer pro-file

• 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 prefer-

ences information– DEF) The preference intensity matrix if matrix for which

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Methodology:


• Step 2-2. optimal preference intensity matrix creation– An optimal preference intensity matrix X– DEF) the optimal preference intensity matrix : preference intensity

matrix

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Methodology:Phase 2 : Ordinal scale-based customer profile creation

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• 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

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Methodology:Phase 2 : Ordinal scale-based customer profile creation


• 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

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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 pur-

chase patterns or coverage– Music j, target customer a

• Exam6) result in exam5.

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Methodology:Phase 3 : neighborhood formation and recommendation generation


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

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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

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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

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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

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• CoFoSIM – viable CF-based recommender system for the mobile web– Enhance the quality of recommendations while mitigating customers’ bur-

den of explicit ratings

• Customers will be able to purchase content with much lower connec-tion 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.

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Conclusion


Discussion

• CF-based recommender + LBS

• Drawbacks

• 분석방법– T-test– ANOVA– MAE(mean absolute error)

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

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