Web Page Recommendation from Sparse Big Web Data

Carson K. Leung, Fan Jiang, Joglas Souza

Abstract: In many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data analytics-and specially, association rule mining or web data mining-is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships-in the form of association rules-among frequently occurring patterns. For instance, in IEEE/WIC/ACM WI 2016 and 2017, serial and parallel algorithms were proposed to find interesting web pages. However, like most of the existing association rule mining algorithms, these two algorithms also were not designed for mining big data. Moreover, the search space of web pages can sparse in the sense that web pages are connected to a small subset of all web pages in the search space. In this paper, we present a compact bitwise representation for web pages in the search space. Such a representation can then be used with a bitwise serial or parallel association rule mining system for web mining and recommendation. Evaluation results show the effectiveness of our compression and the practicality of our algorithm-which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them-in real-life web applications.