An intelligent recommender system using sequential Web access patterns

Abstract

To provide intelligent personalized online services such as web recommendations, it is usually necessary to model users' web acces behavior. To achieve this, onc of the promising approaches is web usage mining, which mines web logs for user models and recommendations. Different from most web recommender systems that are mainly based on clustering and association rule mining, this paper proposes an intelligent web recommender systcm known a 5 SWARS (Sequential Web Accessbascd Recommender System) that uses sequential access pattern mining. In the proposed system, CS-mine, an cffcient sequential pattern mining aIgorithm is used to identify frequent sequential web access patterns. The acces patterns are thcn stored in a compact trce structure, called Pattera-trce, which is then used for matching and generating web links for recommendations. In this paper, the proposed SWARS systcm is described, and its pcrformance is evaluated based on precision, satisfaction and applicability,

A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing

“…If its length is greater than maxlength then we have to remove first (Smaxlength+1) element. If it contains next item then return the recommendation rule order by the support [12].…”

“…Let S = a1a2… akak+1…… an be a web access sequence.For the prefix sequence Sprefix = a1a2… ak (k  MinLength), we generate a recommendation rule RR = {e1, e2, … , em} using the Graph , where all events are ordered by their support [12].…”

Semantically Enriched Web Usage Mining for Predicting User Future Movements

“…The information produced by the system can either be used by the administrator, in order to improve the structure of the Web site, or it can be fed directly to a personalization module to generate recommendations. B. Zhou et al [13] proposed Sequential Web Access-based Recommender System (SWARS) that applies sequential access pattern mining to identify sequential Web access patterns with high frequencies. The Pattern-tree constructed from Web access patterns is used for matching and generating recommendations.…”

An Efficient Web Recommendation System using Collaborative Filtering and Pattern Discovery Algorithms

“…Association rules are applied to above formed groups to find the similar kind of students in future. [17] proposed an intelligent web recommender system namely SWARS (Sequential Web Access-based Recommender System) that uses a sequential pattern mining technique for predicting the next web pages. The paper also proposes a compact data model, called Pattern-tree, which stores the sequential web access patterns, and an efficient approach for user pattern matching and recommendation rules generation.…”