Title: A resource retrieval method of multimedia recommendation system based on deep learning

Authors: Xiaofei Liu; Fei Zhu; Yuchen Fu; Quan Liu

Addresses: School of Computer Science and Technology, Soochow University, Jiangsu Suzhou, 215000, China ' School of Computer Science and Technology, Soochow University, Jiangsu Suzhou, 215000, China ' School of Computer Science and Technology, Soochow University, Jiangsu Suzhou, 215000, China ' School of Computer Science and Technology, Soochow University, Jiangsu Suzhou, 215000, China

Abstract: In order to overcome the problem of too much time and low efficiency in retrieval caused by fuzzy representation of word vectors in multimedia recommendation system, this paper proposes a method of resource retrieval based on deep learning. Firstly, this method preprocesses multimedia resources and retrieval sentences, including morpheme segmentation, stop word processing, term normalisation, stem extraction, word vector processing. In the end, a deep retrieval model is built, to calculate the similarity between multimedia resources and retrieval statements, so as to achieve resource matching. The experimental results show that, while ensuring the accuracy of retrieval, the MAP value of short, medium and long retrieval statement fields is the maximum, which is 0.95, 0.92 and 0.88 respectively. The retrieval time is shortened by 0.3-0.6 s, which improves the retrieval efficiency.

Keywords: deep learning; multimedia recommendation system; resource retrieval; imitate; simulation.

DOI: 10.1504/IJAACS.2020.112606

International Journal of Autonomous and Adaptive Communications Systems, 2020 Vol.13 No.4, pp.400 - 418

Received: 24 Oct 2019
Accepted: 21 Feb 2020
Published online: 22 Jan 2021 *

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