Improved web page recommender system based on web usage mining

Web now becomes the backbone of the information. Today the major concerns are not the availability of information but rather obtaining the right information. Mining the web aims at discovering the hidden and useful knowledge from web hyperlinks, contents or usage logs.This paper focuses on improving the prediction of the next visited web pages and recommends them to the current anonymous user by assigning him to the best navigation profiles obtained by previous navigations of similar interested users.To represent the anonymous user’s navigation history, we used a window sliding method with size n over his current navigation session. Using CTI dataset the experimental results show higher prediction accuracy for the next visited pages for anonymous users compared to previous recommendation system

Prediction of users’ future requests using neural network

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

M-COMMERCE VS. E-COMMERCE: EXPLORING WEB SESSION BROWSING BEHAVIOR

With the growing popularity of mobile commerce (m-commerce), it becomes vital for both researchers and practitioners to understand m-commerce usage behavior. \ \ In this study, we investigate browsing behavior patterns based on the analysis of clickstream data that is recorded in server-side log files. We compare consumers\u27 browsing behaviors in the m-commerce channel against the traditional e-commerce channel. For the comparison, we offer an integrative web usage mining approach, combining visualization graphs, association rules and classification models to analyze the Web server log files of a large Internet retailer in Israel, who introduced m-commerce to its existing e-commerce offerings. \ \ The analysis is expected to reveal typical m-commerce and e-commerce browsing behavior, in terms of session timing and intensity of use and in terms of session navigation patterns. The obtained results will contribute to the emerging research area of m-commerce and can be also used to guide future development of mobile websites and increase their effectiveness. Our preliminary findings are promising. They reveal that browsing behaviors in m-commerce and e-commerce are different

SEMANTICALLY ENRICHED WEB USAGE MINING FOR PREDICTING USER FUTURE MOVEMENTS

ABSTRAC

Augmented Session Similarity Based Framework for Measuring Web User Concern from Web Server Logs

In this paper, an augmented sessions similarity based framework is proposed to measure web user concern from web server logs. This proposed framework will consider the best usage similarity between two web sessions based on accessed page relevance and URL based syntactic structure of website within the session. The proposed framework is implemented using K-medoids clustering algorithms with independent and combined similarity measures. The clusters qualities are evaluated by measuring average intra-cluster and inter-cluster distances. The experimental results show that combined augmented session dissimilarity metric outperformed the independent augmented session dissimilarity measures in terms of cluster validity measures

MINING OF WEB LOG FILES USING RELEVANT COMPUTING TECHNIQUES FOR IMPROVING FUTURE ANTICIPATION USAGE OF WEB NAVIGATION

The Internet has evolved extensively over the past few decades. Web navigation refers to the process of navigating a network of information resources in the World Wide Web, which is organized as hypertext or hypermedia. The navigation related to web navigation usability gets solved by comparing the actual and anticipated usage patterns. The actual usage pattern removed from web server logs are sporadically recorded in operational websites for handling the log data. This process is used to identify the users, user session and user task oriented transactions. The pattern can be discovered among the actual usage path by using the algorithms of data mining generally the ideal user’s interactive path models are framed by cognitive experts based on the cognition of user behavior, which is utilized to pull out the anticipated usage, that includes information about both the time required for user-oriented tasks and the mechanism to identify the user navigation problems here the usability issues get detected from the deviation of the data. It is observed that Genetic algorithms can be used as optimization methods and for corrective action to improve the web navigation usability

Web Usage Mining to Extract Knowledge for Modelling Users of Taiwan Travel Recommendation Mobile APP

This work presents the design of a web mining system to understand the navigational behavior of passengers in developed Taiwan travel recommendation mobile app that provides four main functions including recommend by location , hot topic , nearby scenic spots information , my favorite and 2650 scenic spots. To understand passenger navigational patterns, log data from actual cases of app were collected and analysed by web mining system. This system analysed 58981 sessions of 1326 users for the month of June, 2014. Sequential profiles for passenger navigational patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. The navigational cycle, time, function numbers, and the depth and extent (range) of app were statistically analysed. The analysis results can be used improved the passengers\u27 acceptance of app and help generate potential personalization recommendations for achieving an intelligent travel recommendation service