Privileged contextual information for context-aware recommender systems

• Published in: Expert systems with applications Vol. 57; pp. 139 - 158
• Main Authors: Sundermann, Camila Vaccari, Domingues, Marcos Aurélio, Conrado, Merley da Silva, Rezende, Solange Oliveira
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.09.2016
• Subjects: Ambient intelligence; Construction; Context-aware recommender systems; Contextual information; Domain terms; Expert systems; Hierarchies; Named entities; Recommender systems; Texts; Topic hierarchy; Websites; World Wide Web; Named entities; Domain terms; Context-aware recommender systems; Recommender systems; Topic hierarchy; Contextual information
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2016.03.036

•WA method that treats additional information as virtual items in recommender systems.•The method is instantiated in three different algorithms for recommender systems.•The algorithms are evaluated among themselves and against the state-of-the-art.•Results show that the proposal improves the predictive ability of the recommenders. 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.