0000000000456493

AUTHOR

Maarten De Rijke

showing 3 related works from this author

Social Collaborative Viewpoint Regression with Explainable Recommendations

2017

A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…

ta113Topic modelInformation retrievalComputer sciencetopic modeling02 engineering and technologyRecommender systemtrusted social relationsViewpointsSocial relationRegression020204 information systemsBenchmark (surveying)0202 electrical engineering electronic engineering information engineeringuser comment analysis020201 artificial intelligence & image processingrecommender systemsTupleLatent variable modelProceedings of the Tenth ACM International Conference on Web Search and Data Mining
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Linear Feature Extraction for Ranking

2018

We address the feature extraction problem for document ranking in information retrieval. We then propose LifeRank, a Linear feature extraction algorithm for Ranking. In LifeRank, we regard each document collection for ranking as a matrix, referred to as the original matrix. We try to optimize a transformation matrix, so that a new matrix (dataset) can be generated as the product of the original matrix and a transformation matrix. The transformation matrix projects high-dimensional document vectors into lower dimensions. Theoretically, there could be very large transformation matrices, each leading to a new generated matrix. In LifeRank, we produce a transformation matrix so that the generat…

dimension reductionComputer scienceFeature extractionMathematicsofComputing_NUMERICALANALYSISFeature selectiontiedonhakujärjestelmät02 engineering and technologyLibrary and Information SciencesRanking (information retrieval)Matrix (mathematics)Transformation matrix020204 information systemsalgoritmit0202 electrical engineering electronic engineering information engineeringtiedonhakulearning to rankbusiness.industryfeature extractionPattern recognitionkoneoppiminenPattern recognition (psychology)Benchmark (computing)020201 artificial intelligence & image processingLearning to rankArtificial intelligencebusinessInformation Systems
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Behavior-based personalization in web search

2016

Personalized search approaches tailor search results to users' current interests, so as to help improve the likelihood of a user finding relevant documents for their query. Previous work on personalized search focuses on using the content of the user's query and of the documents clicked to model the user's preference. In this paper we focus on a different type of signal: We investigate the use of behavioral information for the purpose of search personalization. That is, we consider clicks and dwell time for reranking an initially retrieved list of documents. In particular, we (i) investigate the impact of distributions of users and queries on document reranking; (ii) estimate the relevance …

Information Systems and ManagementComputer Networks and CommunicationsComputer sciencehenkilökohtaistaminenInformationSystems_INFORMATIONSTORAGEANDRETRIEVALtiedonhakujärjestelmät02 engineering and technologyLibrary and Information SciencesPersonalizationRanking (information retrieval)Query expansionkustomointiWeb query classification020204 information systems0202 electrical engineering electronic engineering information engineeringRelevance (information retrieval)tiedonhakupersonointiInternetFocus (computing)Information retrievalWeb search queryPersonalized searchRankinghakupalvelut020201 artificial intelligence & image processingInformation SystemsJournal of the Association for Information Science and Technology
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