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Enhancing Collaborative Filtering Using Implicit Relations in Data

  • Chapter
Transactions on Computational Collective Intelligence XXII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9655))

Abstract

This work presents a Recommender System (RS) that relies on distributed recommendation techniques and implicit relations in data. In order to simplify the experience of users, recommender systems pre-select and filter information in which they may be interested in. Users express their interests in items by giving their opinion (explicit data) and navigating through the web-page (implicit data). The Matrix Factorization (MF) recommendation technique analyze this feedback, but it does not take more heterogeneous data into account. In order to improve recommendations, the description of items can be used to increase the relations among data. Our proposal extends MF techniques by adding implicit relations in an independent layer. Indeed, using past preferences, we deeply analyze the implicit interest of users in the attributes of items. By using this, we transform ratings and predictions into “semantic values”, where the term semantic indicates the expansion in the meaning of ratings. The experimentation phase uses MovieLens and IMDb database. We compare our work against a simple Matrix Factorization technique. Results show accurate personalized recommendations. At least but not at last, both recommendation analysis and semantic analysis can be parallelized, alleviating time processing in large amount of data.

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Notes

  1. 1.

    http://grouplens.org/datasets/movielens.

  2. 2.

    http://www.imdb.com/.

  3. 3.

    https://mahout.apache.org.

  4. 4.

    Denote that, since the convergence of the collaborative filtering has been already proved and the semantic approaches do not modify this convergence capability, we do not need a cross-validation set.

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Authors and Affiliations

  1. Institut Supérieur d’Eléctronique de Paris, LISITE Lab, 28, Rue Notre-Dame-des-Champs, 75006, Paris, France

    Manuel Pozo & Raja Chiky

  2. Conservatoire National des Arts Et Métiers, CEDRIC Lab, Paris, France

    Elisabeth Métais

Authors
  1. Manuel Pozo
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  2. Raja Chiky
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  3. Elisabeth Métais
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Correspondence to Manuel Pozo .

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Editors and Affiliations

  1. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, Australia

    Ryszard Kowalczyk

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Pozo, M., Chiky, R., Métais, E. (2016). Enhancing Collaborative Filtering Using Implicit Relations in Data. In: Nguyen, N.T., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXII. Lecture Notes in Computer Science(), vol 9655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49619-0_7

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  • DOI: https://doi.org/10.1007/978-3-662-49619-0_7

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  • Online ISBN: 978-3-662-49619-0

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