Extraction of User Navigation Pattern Based on Particle Swarm Optimization

With current projections regarding the growth of Internet sales, online retailing raises many questions about how to market on the Net. A Recommender System (RS) is a composition of software tools that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Recommender systems are a means of personalizing a site and a solution to the customer?s information overload problem. Recommender Systems (RS) are software tools and techniques providing suggestions for items and/or services to be of use to a user. These systems are achieving widespread success in e-commerce applications nowadays, with the advent of internet. This paper presents a categorical review of the field of recommender systems and describes the state-of-the-art of the recommendation methods that are usually classified into four categories: Content based Collaborative, Demographic and Hybrid systems. To build our recommender system we will use fuzzy logic and Markov chain algorithm

Experimental Investigation of PSO Based Web User Session Clustering

Web user session clustering is very important in web usage mining for web personalization. This paper proposes a Particle Swarm Optimization (PSO) based sequence clustering approach and presents an experimentally investigation of the PSO based sequence clustering methods, which use three original PSO variants and their corresponding variants of a hybrid PSO with real value mutation. The investigation was conducted in 45 test cases using five web user session datasets extracted from a real world web site. The experimental results of these methods are compared with the results obtained from the traditional k-means clustering method. Some interesting observations have been made. In the most of test cases under consideration, the PSO and PSO-RVM methods have better performance than the k-means method. Furthermore, the PSO-RVM methods show better performance than the corresponding PSO methods in the cases in which the similarity measure function is more complex. © 2009 IEEE