A Recommender System for the Web: Using User Profiles and Machine Learning Methods

Abstract

Web development without an integrated structure makes lots of difficulties for users. Web personalization systems are presented to make the website compatible with interest of users in both aspects of contents and services. In this paper extracting user navigation patterns is used to capture similar behaviors of users in order to increase the quality of recommendations. Based on patterns extracted from the same user navigation, recommendations are provided to the user to make it easier to navigate. Recently, web browsing techniques have been widely used for personalization. In this study, a method is proposed to create a user profile with the web usage mining by clustering and neural networks in order to predict the user's future requests and then generate a list of the pages of user's favorites. Simulation results shows that proposed method will increase the accuracy of recommender systems.

A fuzzy method for improving the functionality of search engines based on user's web interactions

“…Because of its rapid and chaotic growth, the resulting network of information does not maintain appropriate organization, which makes the structure of Web sites more complex. When searching and browsing the Web, most users are often overwhelmed by huge amount of information and they are faced with a big challenges to determine the most relevant information at convenient time (Chitraa & Davamani, 2010;Santra & Jayasudha, 2012;Rajabi et al, 2014).…”

A New Profile Learning Model for Recommendation System based on Machine Learning Technique

“…In Genetic Algorithms [8,9], the computational requirements and high runtime were their greatest weakness. In Neural Networks [10,11], time to train NN is probably identified as the biggest disadvantage. In classification technique [12][13][14], they suffer from low accuracy and high computation cost respectively.…”

“…A method is proposed by Rajabi et al [10] to create a user profile using clustering and neural networks in order to predict the user's future requests and then generate a list of the user's preferred pages. Through this study, different user interactions on the web are tracked and then clusters are created based on user interests.…”

“…The application of Machine Learning techniques is a standard way to perform the task of learning user profiles in recommender systems [5], such as Clustering [6,7], Genetic Algorithms [8,9], Neural Networks [10,11], and Classification Techniques [12][13][14][15]. Unfortunately, these techniques suffer from vital drawbacks.…”