Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems

IEEE Transactions on Consumer ElectronicsPublished 1 May 2008

Yolanda Blanco‐Fernández, José J. Pazos‐Arias, Alberto Gil‐Solla, Manuel Ramos‐Cabrer, Martín López‐Nores

DOI10.1109/tce.2008.4560154

Source URL

Citations89

SJR quartileQ1

SJR score1.02

SNIP3.15

TL;DR

This paper presents a strategy that overcomes overspecialization by applying reasoning techniques borrowed from the semantic Web, and discovers a huge amount of knowledge about the user's preferences, and compares them with available products in a more flexible way, beyond the conventional syntactic metrics.

Abstract

Recommender systems arose in view of the information overload present in numerous domains. The so-called content-based recommenders offer products similar to those the users liked in the past. However, due to the use of syntactic similarity metrics, these systems elaborate overspecialized recommendations including products very similar to those the user already knows. In this paper, we present a strategy that overcomes overspecialization by applying reasoning techniques borrowed from the semantic Web. Thanks to the reasoning, our strategy discovers a huge amount of knowledge about the user's preferences, and compares them with available products in a more flexible way, beyond the conventional syntactic metrics. Our reasoning-based strategy has been implemented in a recommender system for interactive digital television, with which we checked that the proposed technique offers accurate enhanced suggestions that would go unnoticed in the traditional approaches.

Keywords

Computer Science

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