Prediction of User Interest by Predicting Product Text Reviews View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Esteban García-Cuesta , Daniel Gómez-Vergel , Luis Gracia-Expósito , José Manuel López-López , María Vela-Pérez

ABSTRACT

Most item shopping websites currently provide social network services (SNS) to collect their users’ opinions on items available for purchasing. This information is often used to reduce information overload and improve both the efficiency of the marketing process and user’s experience by means of user-modeling and hyper-personalization of contents. Whereas a variety of recommendation systems focus almost exclusively on ranking the items, we intend to extend this basic approach by predicting the sets of words that users would use should they express their opinions and interests on items not yet reviewed. To this end, we pay careful attention to the internal consistency of our model by relying on well-known facts of linguistic analysis, collaborative filtering techniques and matrix factorization methods. Still at an early stage of development, we discuss some encouraging results and open challenges of this new approach. More... »

PAGES

132-146

References to SciGraph publications

  • 2007. Collaborative Filtering Recommender Systems in THE ADAPTIVE WEB
  • 2010-10-05. Content-based Recommender Systems: State of the Art and Trends in RECOMMENDER SYSTEMS HANDBOOK
  • 2001-03. Machine Learning for User Modeling in USER MODELING AND USER-ADAPTED INTERACTION
  • Book

    TITLE

    Pattern Recognition Applications and Methods

    ISBN

    978-3-319-93646-8
    978-3-319-93647-5

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-93647-5_8

    DOI

    http://dx.doi.org/10.1007/978-3-319-93647-5_8

    DIMENSIONS

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