A recommendation algorithm based on modified similarity and text content to optimise aggregate diversity. (19th November 2021)

• Record Type: Journal Article
• Authors: Jiang, Shuhao; Zhao, Hongyun; Li, Zhenzhen
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 38:Number 1/3(2021)
• Page Start: 151
• Page End: 157
• Publication Date: 2021-11-19
• Subjects: personalised recommendation -- aggregate diversity -- similarity calculation -- text features; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

With the popularity of smartphones, many people use mobile phones to provide personalised recommendations in a smart city. Aggregate diversity is defined as recommending different categories of items to different users. This paper proposes a personalised recommendation method based on modified similarity and text content. The algorithm optimises the similarity value through modified similarity algorithm, solves the problem of unclear item category by extracting the text features of user browsing. And it clusters according to user category preference, and research and practice personalised recommendation algorithm based on aggregate diversity optimisation. Experimental results show that the proposed algorithm can improve the aggregate diversity of recommendation results while ensuring the accuracy of recommendation.