A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.

Prediction of user future movements and intentions based on the users’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized services, predicting user near future intentions, adaptive Web sites and customer profiling is recognized. A variety of the recommender systems for online personalization through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users’ future intentions systems cannot still satisfy users in the particular huge web sites. In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. The approach is based on the using longest common subsequence algorithm to classify current user activities to predict user next movement. We have tested our proposed model on the CTI datasets. The results indicate that the approach can improve the quality of the system for the predictions

WebPUM : a web-based recommendation system to predict user future movements.

Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy

Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

ConCall: An information service for researchers based on EdInfo

In this paper, we present new types of web information services, where users and information brokers collaborate in creating a user-adaptive information service. Such services impose a novel task on information brokers: they become responsible for maintaining the inference strategies used in user modeling. In return, information brokers obtain more accurate information about user needs, since the adaptivity ensures that user profiles are kept up to date and consistent with what users actually prefer, not only what they say that they prefer. We illustrate the approach by an example application, in which conference calls are collected and distributed to interested readers