A resource retrieval method of multimedia recommendation system based on deep learning. (12th January 2021)

• Record Type: Journal Article
• Authors: Liu, Xiaofei; Zhu, Fei; Fu, Yuchen; Liu, Quan
• Is Part Of: International journal of autonomous and adaptive communications systems. Volume 13:Number 4(2020)
• Page Start: 400
• Page End: 418
• Publication Date: 2021-01-12
• Subjects: deep learning -- multimedia recommendation system -- resource retrieval -- imitate -- simulation; Adaptive computing systems -- Periodicals; Wireless communication systems -- Periodicals; Computer networks -- Periodicals; 004.6
• Languages: English
• ISSNs: 1754-8632

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.