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Semantic Organization of Collaborative Users' Annotations Applied in Recommender Systems


Recommender systems aim to select and present relevant content according to users' preferences, reducing the information overload problem. Among the available techniques, the most known are collaborative and content-based filtering. In addition, there is a trend nowadays to use annotations collaboratively provided by users, such as tags, reviews, comments and interactions, in order to deal with common problems in recommendation, such as overspecialization, cold start and limited content analysis. However, these annotations may contain noise, irony and ambiguity, and may be represented in a unstructured and nonstandardized way. Moreover, there is a lack of a semantic organization of data so that they can be automatically infered to obtain the meaning of related concepts. Thus, this project aims to investigate how to use collaborative annotations produced by users to describe, in semantic terms, the entities involved in recommender systems. To reduce the problems related to using unstructured data, we plan to develop a method that uses different feature extraction, sentiment analysis and machine learning techniques to obtain a rich and standardized representation of items and users' preferences. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
D'ADDIO, RAFAEL M.; MARINHO, RONNIE S.; MANZATO, MARCELO G.. Combining different metadata views for better recommendation accuracy. INFORMATION SYSTEMS, v. 83, p. 1-12, . (16/20280-6)
DE AGUIAR NETO, FERNANDO S.; DA COSTA, ARTHUR F.; MANZATO, MARCELO G.; CAMPELLO, RICARDO J. G. B.. Pre-processing approaches for collaborative filtering based on hierarchical clustering. INFORMATION SCIENCES, v. 534, p. 172-191, . (16/04798-5, 13/07375-0, 16/20280-6)
DA COSTA, ARTHUR F.; MANZATO, MARCELO G.; CAMPELLO, RICARDO J. G. B.. Boosting collaborative filtering with an ensemble of co-trained recommenders. EXPERT SYSTEMS WITH APPLICATIONS, v. 115, p. 427-441, . (16/20280-6)

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