. EFFECTIVE PREDICTION OF WEB-USER ACCESSES: A DATA MINING APPROACH

TLDR

Predicting web‑user accesses is a growing research area with applications in profiling, recommendation, prefetching, and adaptive web design, where the key challenge is developing effective prediction algorithms. This study targets web‑prefetching to reduce user‑perceived latency in web applications. The authors propose a new association‑pattern‑based method that captures all specific characteristics of web‑user navigation. Experimental results show the method outperforms existing approaches and can be readily extended to other applications. Index terms: prediction, web log mining, prefetching, association rules, data mining.

Abstract

The problem of predicting web-user accesses has recently attracted significant attention. Several algorithms have been proposed, which find important applications, like user profiling, recommender systems, web prefetching, design of adaptive web sites, etc. In all these applications the core issue is the developement of an effective prediction algorithm. In this paper, we focus on web-prefetching, because of its importance in reducing user perceived latency present in every Web-based application. The proposed method can be easily extended to the other aforementioned applications. Prefetching refers to the mechanism of deducing forthcoming page accesses of a client, based on access log information. We examine a method that is based on a new type of association patterns, which differently from existing approaches, considers all the specific characteristics of the Web-user navigation. Experimental results indicate its superiority over existing methods. Index Terms — Prediction, Web Log Mining, Prefetching, Association Rules, Data Mining.