Mining and tracking evolving web user trends from large web server logs

Abstract

Abstract:Recently, online organizations became interested in tracking users' behavior on their websites to better understand and satisfy their needs. In response to this need, web usage mining tools were developed to help them use web logs to discover usage patterns or profiles. However, since website usage logs are being continuously generated, in some cases, amounting to a dynamic data stream, most existing tools are still not able to handle their changing nature or growing size. This paper proposes a scalable framework that is capable of tracking the changing nature of user behavior on a website, and represent it in a set of evolving usage profiles. These profiles can offer the best usage representation of user activity at any given time, and they can be used as an input to higher-level applications such as a web recommendation system. Our specific aim is to make the hierarchical unsupervised niche clustering (HUNC) algorithm more scalable, and to add integrated profile tracking and cluster-based validation to it. Our experiments on real web log data confirm the validity of our approach for large data sets that previously could not be handled in one shot. 

Accurate and Diverse Recommendations Based on Communities of Interest and Trustable Neighbors

“…Web usage mining is the process of applying data mining techniques on web log data to analyze the prevalent online activity patterns and extract user profiles from them [25]. HUNC (hierarchical unsupervised niche clustering) is an efficient web usage mining algorithm.…”

Analysing Parallel and Passive Web Browsing Behavior and its Effects on Website Metrics

“…The results in [27] show that users often exhibit different behavior patterns rather than a single one when browsing for information. In [10] the aim was to investigate how the browsing behavior of users changes over time. [20] collected the server accesses on a university router and thus fetching/intercepting the requests to all sites from the intranet to the Web.…”

DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users

“…The results [17] show that users often exhibit different behavior patterns, rather than a single one, when browsing for information. In [8] the aim was to investigate how the browsing behavior of users changes over time. To collect data from more than one site, [14] collected the server accesses on a university router and thus fetching/intercepting the requests to all sites from the intranet to the Web.…”