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Research paper | Open Access

An embedded bandit algorithm based on agent evolution for cold-start problem

Rui Qiu1, Wen Ji2( )
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Science, Beijing, China
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Abstract

Purpose

Many recommender systems are generally unable to provide accurate recommendations to users with limited interaction history, which is known as the cold-start problem. This issue can be resolved by trivial approaches that select random items or the most popular one to recommend to the new users. However, these methods perform poorly in many cases. This paper aims to explore the problem that how to make accurate recommendations for the new users in cold-start scenarios.

Design/methodology/approach

In this paper, the authors propose embedded-bandit method, inspired by Word2Vec technique and contextual bandit algorithm. The authors describe user contextual information with item embedding features constructed by Word2Vec. In addition, based on the intelligence measurement model in Crowd Science, the authors propose a new evaluation method to measure the utility of recommendations.

Findings

The authors introduce Word2Vec technique for constructing user contextual features, which improved the accuracy of recommendations compared to traditional multi-armed bandit problem. Apart from this, using this study's intelligence measurement model, the utility also outperforms.

Practical implications

Improving the accuracy of recommendations during the cold-start phase can greatly raise user stickiness and increase user favorability, which in turn contributes to the commercialization of the app.

Originality/value

The algorithm proposed in this paper reflects that user contextual features can be represented by clicked items embedding vector.

Keywords

Cold-startMulti-armed banditWord2VecIntelligence evolution

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International Journal of Crowd Science
Volume 5 Issue 3,
November 2021
Pages 228-238
DOI: 10.1108/IJCS-03-2021-0005
Cite this article:
Qiu R, Ji W. An embedded bandit algorithm based on agent evolution for cold-start problem. International Journal of Crowd Science, 2021, 5(3): 228-238. https://doi.org/10.1108/IJCS-03-2021-0005

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Received: 02 March 2021
Revised: 06 May 2021
Accepted: 24 May 2021
Published: 05 August 2021
© The author(s)

Rui Qiu and Wen Ji. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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