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HCC-learn framework for hybrid learning in recommender systems

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-030-49559-6_2
AuteurRechercher : ; Rechercher : ; Rechercher : 1
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
FormatTexte, Chapitre de livre
Conférence10th International Joint Conference, IC3K 2018, September 18-20, 2018, Seville, Spain
Sujetrecommender systems; hybrid model; data sparsity; cluster analysis; classification learning
Résumé
In e-business, recommender systems have been instrumental in guiding users through their online experiences. However, these systems are often limited by the lack of labels data and data sparsity. Increasingly, data-mining techniques are utilized to address this issue. In most research, recommendations to be made are achieved via supervised learning that typically employs the k-nearest neighbor learner. However, supervised learning relies on labeled data, which may not be available at the time of learning. Data sparsity, which refers to situations where the number of items that have been recommended represents only a small subset of all available items, further affects model performance. One suggested solution is to apply cluster analysis as a preprocessing step and thus guide the learning process from natural grouping, typically using similar customer profiles, to improve predictive accuracy. In this paper, we study the benefits of applying cluster analysis as a preprocessing step prior to constructing classification models. Our HCC-Learn framework combines content-based analysis in the preprocessing stage and collaborative filtering in the final prediction stage. Our results show the value of our HCC-Learn framework applied to real-world data sets, especially when combining soft clustering and ensembles based on feature subspaces.
Date de publication
Maison d’éditionSpringer
Dans
  • Knowledge Discovery, Knowledge Engineering and Knowledge Management: 10th International Joint Conference, IC3K 2018, Seville, Spain, September 18-20, 2018, Revised Selected Papers (2020) : 25–49.
Série
  • Communications in Computer and Information Science 1222.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement8a20e41a-1d4f-4a0f-a665-195e40784e82
Enregistrement créé2021-06-23
Enregistrement modifié2021-06-24
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