A fuzzy associative classification approach for recommender systems

Despite the existence of dierent methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by means of an alternative data mining technique called classification based on association, which uses association rules in a prediction perspective. In this work we propose a hybrid methodology for recommender systems, which uses collaborative altering and content-based approaches in a joint method taking advantage from the strengths of both approaches. Moreover, we also employ fuzzy logic to enhance recommendations quality and eectiveness. In order to analyze the behavior of the techniques used in our methodology, we accomplished a case study using real data gathered from two recommender systems. Results revealed that such techniques can be applied eectively in recommender systems, minimizing the eects of typical drawbacks they present

Web Usage Mining:A Novel Approach for Web User Session Construction

The growth of World Wide Web is incredible as it can be seen in present days. Web usage mining plays an important role in the personalization of Web services, adaptation of Web sites, and the improvement of Web server performance. It applies data mining techniques to discover Web access patterns from Web log data. In order to discover access patterns, Web log data should be reconstructed into sessions. This paper provides a novel approach for session identification

A study on the personalization methods of the web

Search engine personalization is one of the various deep personalization methods. It can be said that personalization systems that help users find the information they need requires the use of contextual and semantic information analysis techniques that exist in the field of data recovery such as web personalization and the process of optimizing the methods to get to web pages in a way that are consistent with the needs of each user. What helps the current problem of search engines and accelerate their performance, is providing a proper framework for finding the correct pattern considering great items in history of users. This approach improves the advising process of the search engines as well. The aim of this paper is to introduce some process improvement methods of correct patterns and analyze them. Here we will discuss the basic concepts of web personalization and consider the three approaches of web personalization and we evaluated the methods belonging to each of them.Keywords: personalization, search engine, user preferences, data mining method

Design and Evaluation of Techniques to Utilize Implicit Rating Data in Complex Information Systems.

Research in personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users - where it is possible to classify items involved and to make personalization based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as Digital Libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. In this report, we present techniques for collecting, storing, processing, and utilizing implicit rating data of Digital Libraries for analysis and decision support. We present our pilot study to find virtual user groups using implicit rating data. We demonstrate the effectiveness of implicit rating data for characterizing users and finding virtual user communities, through statistical hypothesis testing. Further, we describe a visual data mining tool named VUDM (Visual User model Data Mining tool) that utilizes implicit rating data. We provide the results of formative evaluation of VUDM and discuss the problems raised and plans for further studies

Book Recommendation System using Data Mining for the University of Hong Kong Libraries

This paper describes the theoretical design of a Library Recommendation System, employing k- means clustering Data Mining algorithm, with subject headings of borrowed items as the basis for generating pertinent recommendations. Sample data from the University of Hong Kong Libraries (HKUL) has been used in a Quantitative approach to study the existing Library Information System, Innopac. Data Warehousing and Data Mining (k-means clustering) techniques are discussed. The primary benefit of the system is higher quality of academic research ensuing from better search results. Personalization improves individual effectiveness of learners and overall in better utilizing library resources.published_or_final_versio

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Personalization by Partial Evaluation.

The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality