Download - 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
S.-K. Lee et al., KAIST, Information Sciences, Vol. 180, Issue 11, pp. 2142-2155, 2010.
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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:
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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:
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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:
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• 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
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• 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
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• 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
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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:
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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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Experimental results:
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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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Experimental results:
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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
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Discussion
• CF-based recommender + LBS
• Drawbacks
• 분석방법– T-test– ANOVA– MAE(mean absolute error)
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