Journal ArticleDOI
A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems
Yolanda Blanco-Fernández,José J. Pazos-Arias,Alberto Gil-Solla,Manuel Ramos-Cabrer,Martín López-Nores,Jorge García-Duque,Ana Fernández-Vilas,Rebeca P. Díaz-Redondo,Jesús Bermejo-Muñoz +8 more
TLDR
This paper proposes a personalization strategy that overcomes drawbacks in recommender systems by applying inference techniques borrowed from the Semantic Web, and illustrates its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.Abstract:
Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.read more
Citations
More filters
Journal ArticleDOI
A literature review and classification of recommender systems research
TL;DR: This research provides information about trends in recommender systems research by examining the publication years of the articles, and provides practitioners and researchers with insight and future direction on recommender system research.
Journal ArticleDOI
Ontology-based information content computation
TL;DR: This paper analyzes ontology-based approaches for IC computation and proposes several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept.
Journal ArticleDOI
Social knowledge-based recommender system. Application to the movies domain
Walter Carrer-Neto,Maria Luisa Hernández-Alcaraz,Rafael Valencia-García,Francisco García-Sánchez +3 more
TL;DR: A hybrid recommender system based on knowledge and social networks is presented and its evaluation in the cinematographic domain yields very promising results compared to state-of-the-art solutions.
A Review of Semantic Similarity Measures in WordNet 1
TL;DR: The paper contains a review of the state of art measures, including path Based measures, information based measures, feature based measures and hybrid measures, and the area of future research is described.
Journal ArticleDOI
A hybrid recommendation approach for a tourism system
Joel Pinho Lucas,Nuno Luz,María N. Moreno,Ricardo Anacleto,Ana Maria de Almeida Figueiredo,Constantino Martins +5 more
TL;DR: This work implements a recommendation methodology in a recommender system for tourism, where classification based on association is applied, which is able to shorten limitations presented in recommender systems and to enhance recommendation quality.
References
More filters
Proceedings Article
An Information-Theoretic Definition of Similarity
TL;DR: This work presents an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model and demonstrates how this definition can be used to measure the similarity in a number of different domains.
Proceedings ArticleDOI
Verb semantics and lexical selection
Zhibiao Wu,Martha Palmer +1 more
Abstract: This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
Journal ArticleDOI
Fab: content-based, collaborative recommendation
Marko Balabanovic,Yoav Shoham +1 more
TL;DR: It is explained how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither, and how the particular design of the Fab architecture brings two additional benefits.
Journal ArticleDOI
GroupLens: applying collaborative filtering to Usenet news
Joseph A. Konstan,Bradley N. Miller,David A. Maltz,Jonathan L. Herlocker,Lee R. Gordon,John Riedl +5 more
TL;DR: The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high.
Journal ArticleDOI
Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language
TL;DR: In this paper, a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content is presented, and experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge counting approach.