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Authors: Alan V. Prando 1 ; Felipe G. Contratres 2 ; Solange N. A. Souza 2 and Luiz S. de Souza 3

Affiliations: 1 Instituto de Pesquisas Tecnológicas, Brazil ; 2 Universidade de São Paulo, Brazil ; 3 Faculdade de Tecnologia, Brazil

ISBN: 978-989-758-271-4

Keyword(s): Recommender System, Social Networks, Cold-Start, Content-based Recommendations.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Intelligence Applications ; Data Mining in Electronic Commerce ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: Recommender systems have been widely applied to e-commerce to help customers find products to purchase. Cold-start is characterized by the incapability of recommending due to the lack of enough ratings. In fact, solutions for the cold-start problem have been proposed for different contexts, but the problem is still unsolved. This paper presents a RS for new e-commerce users by using only their interactions in social networks to understand their preferences. The proposed Recommender System (RS) applies a content-based approach and improves the experience of new users by recommending specific products in a preferred identified user category by analysing their data from the social network. Therefore, it combines three social network elements: (1) direct user posts (e.g.: "tweets" from Twitter and "posts" from Facebook), (2) content "likes" (e.g.: option "like" on a "post" or "tweet" posted by another user), and (3) page "likes" (e.g.: option "like" on a Facebook page). The proposed RS was tested for a retail e-commerce, which usually not only has a large range of categories of products, but also has products within these categories. The difficulty in predicting a product increases sharply with a greater number of categories and products. According to the experiment conducted, the proposed RS demonstrated to be a reasonable alternative to cold-start, i.e., for users accessing e-commerce for the very first time. (More)

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Paper citation in several formats:
Prando, A.; Contratres, F.; Souza, S. and de Souza, L. (2017). Content-based Recommender System using Social Networks for Cold-start Users.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-271-4, pages 181-189. DOI: 10.5220/0006496301810189

@conference{kdir17,
author={Alan V. Prando. and Felipe G. Contratres. and Solange N. A. Souza. and Luiz S. de Souza.},
title={Content-based Recommender System using Social Networks for Cold-start Users},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2017},
pages={181-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006496301810189},
isbn={978-989-758-271-4},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Content-based Recommender System using Social Networks for Cold-start Users
SN - 978-989-758-271-4
AU - Prando, A.
AU - Contratres, F.
AU - Souza, S.
AU - de Souza, L.
PY - 2017
SP - 181
EP - 189
DO - 10.5220/0006496301810189

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