Search results for "Recommender Systems"

showing 3 items of 23 documents

A Serendipity-Oriented Greedy Algorithm for Recommendations

2017

Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …

ta113SerendipityComputer sciencebusiness.industrysuosittelujärjestelmät020207 software engineeringserendipity02 engineering and technologyalgorithmsunexpectednessnoveltyalgoritmit0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencerecommender systemsGreedy algorithmbusinessGreedy randomized adaptive search procedure
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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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Listwise Collaborative Filtering

2015

Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting th…

ta113business.industryComputer scienceRecommender systemMachine learningcomputer.software_genreRankingcollaborative filteringBenchmark (computing)Collaborative filteringProbability distributionPairwise comparisonData miningArtificial intelligencerecommender systemsbusinessFocus (optics)computerranking-oriented collaborative filtering
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