Prediction of User Opinion for Products - A Bag-of-Words and Collaborative Filtering based Approach

Esteban García-Cuesta, Daniel Gómez-Vergel, Luis Gracias Expósito, María Vela-Pérez
<span title="">2017</span> <i title="SCITEPRESS - Science and Technology Publications"> <a target="_blank" rel="noopener" href="" style="color: black;">Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods</a> </i> &nbsp;
The rapid proliferation of social network services (SNS) gives people the opportunity to express their thoughts, opinions, and tastes on a wide variety of subjects such as movies or commercial items. Most item shopping websites currently provide SNS systems to collect users' opinions, including rating and text reviews. In this context, user modeling and hyper-personalization of contents reduce information overload and improve both the efficiency of the marketing process and the user's overall
more &raquo; ... tisfaction. As is well known, users' behavior is usually subject to sparsity and their preferences remain hidden in a latent subspace. A majority of recommendation systems focus on ranking the items by describing this subspace appropriately but neglect to properly justify why they should be recommended based on the user's opinion. In this paper, we intend to extract the intrinsic opinion subspace from users' text reviews -by means of collaborative filtering techniquesin order to capture their tastes and predict their future opinions on items not yet reviewed. We will show how users' reviews can be predicted by using a set of words related to their opinions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.5220/0006209602330238</a> <a target="_blank" rel="external noopener" href="">dblp:conf/icpram/Garcia-CuestaGE17</a> <a target="_blank" rel="external noopener" href="">fatcat:hnfx54cnqfeatdpml2muf26ska</a> </span>
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