Privileged contextual information for context-aware recommender systems

Autor(es)

Sundermann, Camila Vaccari ^[1] ; Domingues, Marcos Aurelio ^[2] ; Conrado, Merley da Silva ^[1] ; Rezende, Solange Oliveira ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Ave Trabalhador Sao Carlense, BR-13566590 Sao Carlos, SP - Brazil

^[2] Univ Estadual Maringa, Dept Informat, Ave Colombo 5790, BR-87020900 Maringa, PR - Brazil

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 57, p. 139-158, SEP 15 2016.
• Citações Web of Science: 10

Resumo

A recommender system is used in various fields to recommend items of interest to the users. Most recommender approaches focus only on the users and items to make the recommendations. However, in many applications, it is also important to incorporate contextual information into the recommendation process. Although the use of contextual information has received great focus in recent years, there is a lack of automatic methods to obtain such information for context-aware recommender systems. Some works address this problem by proposing supervised methods, which require greater human effort and whose results are not so satisfactory. In this scenario, we propose an unsupervised method to extract contextual information from web page content. Our method builds topic hierarchies from page textual content considering, besides the traditional bag-of-words, valuable information of texts as named entities and domain terms (privileged information). The topics extracted from the hierarchies are used as contextual information in context-aware recommender systems. We conducted experiments by using two data sets and two baselines: the first baseline is a recommendation system that does not use contextual information and the second baseline is a method proposed in literature to extract contextual information. The results are, in general, very good and present significant gains. In conclusion, our method has advantages and innovations:(i) it is unsupervised; (ii) it considers the context of the item (Web page), instead of the context of the user as in most of the few existing methods, which is an innovation; (iii) it uses privileged information in addition to the existing technical information from pages; and (iv) it presented good and promising empirical results. This work represents an advance in the state-of-the-art in context extraction, which means an important contribution to context-aware recommender systems, a kind of specialized and intelligent system. (C) 2016 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 13/16039-3 - Exploração de técnicas de mineração de textos para aquisição automática de informação contextual para sistemas de recomendações sensíveis ao contexto
• Beneficiário: Camila Vaccari Sundermann
• Modalidade de apoio: Bolsas no Brasil - Mestrado