Utility-based Recommender Systems Using Implicit Utility and Genetic Algorithm

DOI

10.2991/meic-15.2015.197How to use a DOI?

Keywords

recommended system; multi-attribute utility; implicit utility; genetic algorithm; browsing behavior

Abstract

Recommender systems are used to recommend items for user in e-commerce with information overload. Utility-based recommender systems build multi-attribute utility function of user and recommend the highest utility item for user. Some utility-based recommender systems use rating for items to extract utility function, which produce significant burden for user. The paper proposes a utility-based recommender technique which can predict attribute value utility and implicit holistic utility rate of items by user browsing behavior and genetic algorithm, and elicit the attribute weight by genetic algorithm, and building a multi-attribute utility function. The experimental results on clothing recommendation show that the proposed method is superior to current utility-based methods on accuracy, satisfaction, usefulness and time expense.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Feng Deng
PY  - 2015/04
DA  - 2015/04
TI  - Utility-based Recommender Systems Using Implicit Utility and Genetic Algorithm
BT  - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 860
EP  - 864
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-15.2015.197
DO  - 10.2991/meic-15.2015.197
ID  - Deng2015/04
ER  -