Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems. (22nd February 2016)

• Record Type: Journal Article
• Authors: Mahboob, Vahideh Amel; Jalali, Mehrdad; Jahan, Majid Vafaei; Barekati, Pegah
• Is Part Of: Computational intelligence. Volume 33:Number 1(2017)
• Page Start: 99
• Page End: 118
• Publication Date: 2016-02-22
• Subjects: social annotation systems, web recommender systems, heat diffusion, graph; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12086
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called "Swallow" creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.