Improved Reinforcement-Based Profile Learning For Document Filtering

Today the amount of accessible information is overwhelming. A personalized information filtering system must be able to tailor to current interests of the user and to adapt as they change over time. This system has to monitor a stream of incoming documents to learn the user’s information requirements, which is the user profile. The research has proposed a content-based personal information system learns the user’s preferences by analyzing the document contents and building a user profile. This system is called RePLS; an agent-based Reinforcement Profile Learning System with adaptive information filtering. The research focuses on an improved terms weighting to measure the importance of the terms represent each profile called “purity term weighting”. The top selected terms are then used to filter the incoming documents to the learned user profiles. The agent approach is used because of its autonomous and adaptive capabilities to perform the filtering. The proposed method was evaluated and compared with three Information Filtering methods, namely Rocchio, Okapi/BSS Basic Search System and Reinf, the incremental profile learning method. Based on the proposed method, a profile learning system is developed using Microsoft VC++ connected to Microsoft Access database through an ODBC. AFC kit is used to implement the proposed agents under RETSINA architecture. The experiments are carried out on the TREC 2002 Filtering Track dataset provided by the National Institute of Standards and Technology (NIST). This research has proven that RePLS is able to filter the stream of incoming documents according to the user interests (profiles) learned by the proposed Purity term weighting method. Based on the experiments results, Purity weighting shows better terms weighting and profile learning than the other methods. The outcome of a considerably good accuracy is mainly due to the right weighting of the profile’s terms during the learning phase. This research opens a wide range of future works to be considered, including the investigation of the dependency between the selected terms for each profile, investigating the quality of the method on different datasets, and finally, the possibility to apply the proposed method in other area like the recommendation systems

Adaptive method to improve web recommendation system for anonymous users

Today the major concerns are not the availability of information but rather obtaining the right information. Web Recommendation system is a specific type of Web personalization system technique that attempts to predict the user next browsing activity then recommend the web pages items that are likely to be of interest to the user. The ability of predicting the next visited pages and recommending it to the short term navigation user (anonymous user) is highly needed. Presently, there are many recommendation systems (e.g. Analog, Web Miner, WebPersonalizer, PACT,SWARS, EntreeC, SUGGEST, one-and-only items, Hybrid and NEWER) that can be used to make recommendation to the current online user, but, recommendation to anonymous users needs to be adaptive (up to date) to the changes in users‟ interests‟ over time. This research focuses on improving the prediction of the next visited web pages and introduces them to current anonymous user. An enhanced classification algorithm is used to assign the current anonymous user to the best web navigation profile. As the users‟ interests change over time, the recommender system has the ability to modify the current web navigation profiles and keep them updated. These adaptive profiles help the prediction engine to predict and then recommend the next visited pages to the current user in an accurate manner. This research proposed two web page recommendation systems. The first is iPACT, an improved recommendation system based on PACT methodology to demonstrate the prediction accuracy of the proposed enhanced classification algorithm in this research. The prediction accuracy was evaluated against two previous recommendation systems PACT and HyperGraph. The second is Adaptive Web page Recommendation System (AWRS) which combines the classification algorithm of iPACT in addition to the ability of adaptive recommending due to the changes of the users‟ interests and weighting methods to deal with unvisited or new added pages. For the evaluating purpose, the experiments were carried out on the public CTI logs file dataset which contains the preprocessed and filtered sessionized data for the main DePaul CTI Web server. AWRS was evaluated and shows better performance as compared to several recommendation systems namely, iPACT, Association Rules and Hybrid systems. Based on the experimental results, the outcome of a considerably good accuracy is mainly due to the right classification of the current user to the best web navigation profile with similar browsing activities. Also, the adaptive phase is able to update the web navigation profile(s) based on the interest‟s changes and predict the next visited pages in accurate manner to the anonymous users based on their early stage navigation. This research opens a wide range of future works to be considered,including the investigation of the dependency between the recommended web pages for each web navigation profile, investigating the quality of the method on different datasets, and finally, the possibility to apply the proposed method in other area like the misuse detection systems

iPACT: improved web page recommendation system using profile aggregation based on clustering of transactions

Problem statement: Recently, Web usage mining techniques have been widely used to build recommendation systems especially for anonymous users. Approach: Assigning the current user to the best web navigation profile with similar navigation activities will improve the ability of the prediction engine to produce a recommendation list then introduce it to the user. This study presents iPACT an improved recommendation system using Profile Aggregation based on Clustering of Transactions (PACT). Results: iPACT shows better prediction accuracy than the previous methods PACT and Hypergraph. Conclusion: The users interests change over time; hence an incremental and adaptive web navigation profiling is a key feature for the future works