Leveraging contextual information for cold‐start Web service recommendation. (17th February 2019)

• Record Type: Journal Article
• Authors: Tian, Gang; Wang, Qibo; Wang, Jian; He, Keqing; Zhao, Weidong; Gao, Panpan; Peng, Yanjun
• Is Part Of: Concurrency and computation. Volume 31:Number 17(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-02-17
• Subjects: cold‐start web service recommendation -- implicit feedback -- probability matrix factorization; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5195
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Web service recommendation becomes an increasingly important issue when more and more services are published on the Internet. Many Web service recommendation methods have been proposed in recent years, most of which adopted collaborative filtering (CF) techniques. In general, these approaches have two limitations. Firstly, they rarely leverage user ratings since this kind of explicit feedback is difficult to collect for Web services. Secondly, the new user cold‐start problem is an inherent limitation of CF because the new users have not yet cast sufficient numbers of votes. In this paper, pseudo ratings of services constructed based on plenty of user‐service interactions, also known as a kind of implicit feedback, are provided to represent users' preferences on services. Based on these pseudo ratings, we present a novel Web service recommendation approach, which can alleviate the cold‐start problem by integrating contextual information and an online learning model. Experiments conducted on a real world data set show that, compared with the method without contextual information, our proposed approach that handles the cold start problem by integrating contextual information can achieve better F‐Measure performance (5.08 times increase on average). Moreover, the proposed online recommendation approach can dramatically decrease the time overhead while keeping the similar recommendation performance.