building and evaluating a location-based service recommendation system with a preference adjustment...
TRANSCRIPT
Building and Evaluating a Location-Based Service
Recommendation System with a Prefer-ence
Adjustment mechanism
Expert Systems with Applications, vol. 36, no. 2, pp. 3543-3554, 2009
M.-H. Kuo et al.
Introduction
• Mobile commerce(M-Commerce) is developing trend due to successful experience of E-Commerce(EC)
• Most significant difference between M-Commerce and EC
mobility feature of mobile device
• Studies focusing on location data are created characterized as location-based service(LBS)
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Previous Works
• Basic theme is based on that relevant information changes according to the location of mobile customers (Chen, 2002)
• The service have been adopted for various purpose– L-PRS: a location-based personalized recommender system (Kim, Song, & Yang, 2003)
• The key for LBS is the development of interface design and ability to provide correct and real-time content
– A user-oriented contents recommendation system in peer-to-peer architecture (Kim, Kim, & Cho, 2008)
Preference adjustment is necessary to recommendation system
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Goal of this research
• Establishing a location-based information recommend system
– Integrating geographical location and personnel prefer-ence
• Developing and measuring a personalized prototype system based on location based service recommenda-tion model(LBSRM)
– Recommend hotel information
• Designing an experimental method for effectiveness of
preference adjustment– Long-term preference– Short-term preference
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Recommendation model for LBS
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Location-based database
• is an attribute set of LBS items
• Dataset : including dynamic and static attribute– Dynamic : - numeric type, catalogic type
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User preference database
• Include user static data and dynamic data• : preference cluster of location based information of
different users
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User history database
• Database record historical items user has selected
• Including contents– Mobile device identification code– System recommended items– User actually selected items– Content of each item
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Recommendation module
• Area data filtering
– D : search area, (Xu, Yu) : user location
• Information grouping
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Recommendation score calculating
• Score of numeric type
• Score of catalogic type
• Total recommendation score
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r : rate of time-discountp : total number of recom- mendation itemsq : number of history record
Preference adjustment : Short-term ad-justment
• Use the difference of the recommendation score
• To prevent user preference goes to 0
• : recommendation score when the user selects item C• : recommendation score when the most recommended
information is item A
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Preference adjustment : Long-term ad-justment
• Use Bayes’s decision procedure
• Add time discount rate ( r )
– : group number of items : the number of items oc-curred
: user selecting item of : system recommendation item : the sum of recommenda-
tion numbers
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Development of prototype system
• User location based hotel recommendation system
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Development of prototype system
• User location based hotel recommendation system
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Development of prototype system
• User location based hotel recommendation system
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Development of prototype system
• User registration
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C.P number
3 preference of att. ofnumeric type
(distance, pricing, ser-vice)
Att. of catalogic type
multiple-choice
Development of prototype system
• Recommendation simulation system
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Electronic map
C.P Inter-face
Recommendation simulation system• User location selection
– Randomly pick a location in order to simulate mobile environment
– Search all hotels with in search range– Using Euclidean Distance
• Recommendation step– Dived into three groups
• distance(D), Price(P) and service(S)– Take recommendation
• Calculation of the scores and display at the table• User can click “details’ and see further discription
• Preference adjustment step– Get the user feedback– Recommendation success or – Preference adjustment is then undertaken
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Evaluation of prototype system
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Evaluation of prototype system
• Satisfaction of system recommendation– Precision of recommendation
– On-line questionnaire is generated for the first 10 items• 35 registrants. Each respondent conducted for six times• Respondent : MBA student enrolled in National Defense Uni-
versity ,Taiwan
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Evaluation of prototype system
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Evaluation of prototype system
• The efficiency of preference adjustment– Short-term preference
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Evaluation: Efficiency of preference ad-justment
• Short-term preference
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Lack of sta-bility
Evaluation: Efficiency of preference ad-justment
• Long-term preference
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Evaluation: Efficiency of preference ad-justment
• Long-term preference
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1. The system reaction to preference ad-justment is immediate if without his-tory data
2. Latter adjustment(i.e., the more history data), the more needed number of adjustment
3. The lower the discount rate, the faster the learning adjustment speed
Evaluation: Efficiency of preference ad-justment
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Evaluation: Efficiency of preference ad-justment
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Evaluation: Efficiency of preference ad-justment
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Conclusion
• Proposed a model combines LBS and information recom-mendation
• Designed prototype system of preference adjustment
• Evaluated the effectiveness of the LBSRM as adopt the on-line questionnaires
– Proved that the expected recommendation effect is to be achieved
• Evaluated the efficiency of the preference adjustment
• Regard to the adjusting ability, long-term preference can reach better result
• Number of preference adjustment could be determined by changing the weight of recent preference(i.e., dynamic method of time-dicount rate)
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