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Authors: Konstantinos N. Vavliakis 1 ; Maria Th. Kotouza 2 ; Andreas L. Symeonidis 2 and Pericles A. Mitkas 2

Affiliations: 1 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece, Pharm24.gr and Greece ; 2 Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki and Greece

Keyword(s): Personalization, Recommendation, Conversational Web, e-Commerce, RFM, Recurrent Neural Networks.

Related Ontology Subjects/Areas/Topics: Enterprise Information Systems ; Recommendation Systems ; Software Agents and Internet Computing

Abstract: In this paper we redefine the concept of Conversation Web in the context of hyper-personalization. We argue that hyper-personalization in the WWW is only possible within a conversational web where websites and users continuously “discuss” (interact in any way). We present a modular system architecture for the conversational WWW, given that adapting to various user profiles and multivariate websites in terms of size and user traffic is necessary, especially in e-commerce. Obviously there cannot be a unique fit-to-all algorithm, but numerous complementary personalization algorithms and techniques are needed. In this context, we propose PRCW, a novel hybrid approach combining offline and online recommendations using RFMG, an extension of RFM modeling. We evaluate our approach against the results of a deep neural network in two datasets coming from different online retailers. Our evaluation indicates that a) the proposed approach outperforms current state-of-art methods in small-medium d atasets and can improve performance in large datasets when combined with other methods, b) results can greatly vary in different datasets, depending on size and characteristics, thus locating the proper method for each dataset can be a rather complex task, and c) offline algorithms should be combined with online methods in order to get optimal results since offline algorithms tend to offer better performance but online algorithms are necessary for exploiting new users and trends that turn up. (More)

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Paper citation in several formats:
Vavliakis, K.; Kotouza, M.; Symeonidis, A. and Mitkas, P. (2018). Recommendation Systems in a Conversational Web. In Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-324-7; ISSN 2184-3252, SciTePress, pages 68-77. DOI: 10.5220/0006935300680077

@conference{webist18,
author={Konstantinos N. Vavliakis. and Maria Th. Kotouza. and Andreas L. Symeonidis. and Pericles A. Mitkas.},
title={Recommendation Systems in a Conversational Web},
booktitle={Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST},
year={2018},
pages={68-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006935300680077},
isbn={978-989-758-324-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST
TI - Recommendation Systems in a Conversational Web
SN - 978-989-758-324-7
IS - 2184-3252
AU - Vavliakis, K.
AU - Kotouza, M.
AU - Symeonidis, A.
AU - Mitkas, P.
PY - 2018
SP - 68
EP - 77
DO - 10.5220/0006935300680077
PB - SciTePress