Recommended System for Enhancing Tag Popularity in a Question Answering Community through Splay-net Techniques
Jayashree R
Jayashree. R*, Department of Computer Applications, School of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur campus, Chennai, India.
Manuscript received on November 22, 2020. | Revised Manuscript received on November 25, 2020. | Manuscript published on December 30, 2020. | PP: 43-48 | Volume-10 Issue-2, December 2020. | Retrieval Number: 100.1/ijeat.B20051210220 | DOI: 10.35940/ijeat.B2005.1210220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Collaborative filtering filters information by using the recommendations of peer participants. The long tail problem states users with higher points obtain a high reputation compared to less scored users. In popular community question answering websites, like stack exchange network sites, users with unanswered or ignored questions for a long time get a tumbleweed badge without considering their past history. This deteriorates their further contribution to the website. Mostly new or low-reputation people ask the tumbleweed questions. The popularity of the tags follows a long tail theory. The focus of this research work is to design a recommendation system that prevents participants from tumbleweed badge with tag suggestion method to add new or non-popular tags to the existing popular tag list. The splay-net has a self-balancing graph which brings the recently accessed item to the top of the tree. In this paper, we use the splay-net technique to represent users’ reputation along with their tags.
Keywords: Collaborative filtering; classification; Learning; Ranking system; splay Tree Data Structure.
Scope of the Article: classification