learning to recommend questions based on user ratings ke sun, yunbo cao, xinying song, young-in...
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Learning to Recommend Learning to Recommend Questions Based on User Questions Based on User
RatingsRatings
Ke Sun, Yunbo Cao, Xinying Song,
Young-In Song, Xiaolong Wang and Chin-Yew Lin.
In Proceeding of the 18th ACM Conference on Information and Knowledge Management
(Hong Kong, China, November 02 - 06, 2009).
Prepared and Presented by Baichuan Li
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OutlineOutlineIntroductionProblem StatementAlgorithmsExperimentsConclusion
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IntroductionIntroductionCommunity-Based Question-
Answering (CQA) Services
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Finding AnswersFinding Answers
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Query
Existed similar
questions and their answers
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Finding QuestionsFinding Questions
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Sort by popularity
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PROBLEM PROBLEM STATEMENTSTATEMENT
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RecommendationRecommendation
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Preference Preference OOrderrder
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Ordered PairsOrdered Pairs
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Ranking FunctionRanking Function
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PrinciplePrinciple
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ALGORITHMSALGORITHMS
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The Perceptron Algorithm for The Perceptron Algorithm for Preference Learning (PAPL)Preference Learning (PAPL)
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The Majority-Based The Majority-Based Perceptron Algorithm (MBPA)Perceptron Algorithm (MBPA)
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EXPERIMENTSEXPERIMENTS
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DatasetDataset297,919 questions under ‘travel’
category at Yahoo! Answers
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Dataset (Cont.)Dataset (Cont.)
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Dataset (Cont.)Dataset (Cont.)
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ResultsResultsEvaluation Measure
◦Error rate of preference pairs
Result
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Results (Cont.)Results (Cont.)
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ConclusionConclusionInvestigated the problem of
learning to recommend questions based on user ratings◦Enlarged the size of available
training data through adding questions without user rating
◦Demonstrated the approach’s effectiveness through intensive experiments
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