Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided.

The World Wide Web is a great source of information, which is nowadays being widely used due to the availability of useful information changing, dynamically. However, the large number of webpages often confuses many users and it is hard for them to find information on their interests. Therefore, it is necessary to provide a system capable of guiding users towards their desired choices and services. Recommender systems search among a large collection of user interests and recommend those, which are likely to be favored the most by the user. Web usage mining was designed to function on web server records, which are included in user search results. Therefore, recommender servers use the web usage mining technique to predict users’ browsing patterns and recommend those patterns in the form of a suggestion list. In this article, a recommender system based on web usage mining phases (online and offline) was proposed. In the offline phase, the first step is to analyze user access records to identify user sessions. Next, user profiles are built using data from server records based on the frequency of access to pages, the time spent by the user on each page and the date of page view. Date is of importance since it is more possible for users to request new pages more than old ones and old pages are less probable to be viewed, as users mostly look for new information. Following the creation of user profiles, users are categorized in clusters using the Fuzzy C-means clustering algorithm and S(c) criterion based on their similarities. In the online phase, a neural network is offered to identify the suggested model while online suggestions are generated using the suggestion module for the active user. Search engines analyze suggestion lists based on rate of user interest in pages and page rank and finally suggest appropriate pages to the active user. Experiments show that the proposed method of predicting user recent requested pages has more accuracy and cover than other methods.

The rapid growth of the web and the lack of structure or an integrated schema create various issues to access the information for users. All users’ access on web information are saved in the related server log files. The circumstance of using these files is implemented as a resource for finding some patterns of user & apos; s behavior. Web mining is a subset of data mining and it means the mining of the related data from WWW, which is categorized into three parts including web content mining, web structure mining and web usage mining, based on the part of data, which is mined. It seems necessary to have a technique, which is capable of learning the users’ interests and based on the interests, which could filter the unrelated interests automatically or it could offer the related information to the user in reasonable amount of time. The web usage mining makes a profile from users to recognize them and it has direct relationship to web personalizing. The primary objective of personalizing systems is to prepare the thing, which is required by users, without asking them explicitly. In the other way, formal models prepare the possibility of system’s behavior modeling. The Petri and queue nets as some samples of these models can analyze the user & apos; s behavior in web. The primary objective of this paper is to present a colored Petri net to model the user & apos; s interactions for offering a list of pages recommendation to them in web. Estimating the user & apos; s behavior is implemented in some cases like offering the proper pages to continue the browse in web, ecommerce and targeted advertising. The preliminary results indicate that the proposed method is able to improve the accuracy criterion 8.3% rather static method.

This paper presents a method to classify the web user’s navigation patterns automatically. The proposed model of this paper classifies user’s navigation patterns and predicts his/her upcoming requirements. To create users’ profile, a new method is introduced by recording user’s settings active and user’s similarity measurement with neighboring users. The proposed model is capable of creating the profile implicitly. Besides, it updates the profile based on created changes. In fact, we try to improve the function of recommender engine using user’s navigation patterns and clustering. The method is based on user’s navigation patterns and is able to present the result of recommender engine based on user’s requirement and interest. In addition, this method has the ability to help customize websites, more efficiently.

With the rapid growth of the World Wide Web, finding useful information from the Internet has become a critical issue. Automatic classification of user navigation patterns provides a useful tool to solve these problems. In this paper, we propose an approach for classification of users’ navigation patterns and prediction of users’ future requests. Users’ profiles are constructed based on Web log server files and one of clustering methods is implemented to users’ profiles for assigning navigation patterns. Finally, using neural network, recommender engine produces a relevant recommendation list of web pages to the active user. The preliminary results indicate that the proposed approach has high accuracy and coverage in prediction of users’ future requests.