A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table

The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources

Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks

Enhanced information retrieval by exploiting recommender techniques in cluster-based link analysis

Inspired by the use of PageRank algorithms in document ranking, we develop and evaluate a cluster-based PageRank algorithm to re-rank information retrieval (IR) output with the objective of improving ad hoc search effectiveness. Unlike existing work, our methods exploit recommender techniques to extract the correlation between documents and apply detected correlations in a cluster-based PageRank algorithm to compute the importance of each document in a dataset. In this study two popular recommender techniques are examined in four proposed PageRank models to investigate the effectiveness of our approach. Comparison of our methods with strong baselines demonstrates the solid performance of our approach. Experimental results are reported on an extended version of the FIRE 2011 personal information retrieval (PIR) data collection which includes topically related queries with click-through data and relevance assessment data collected from the query creators. The search logs of the query creators are categorized based on their different topical interests. The experimental results show the significant improvement of our approach compared to results using standard IR and cluster-based PageRank methods

Divertimi: A Tourist Guide to a Unique and Enriching Experience

This project lays a foundation for the development of an e-tourism website by Azienda di Promozione Turistica della Provincia di Venezia, the provincial tourism authority in the Veneto region of Italy. Our design employs individual and group profiling to recommend destinations and attractions. Social networking and various forms of user-generated narratives support travel recommendations. Finally, we propose a system for offering a personalized trip package based on user interests

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

THE USE OF RECOMMENDER SYSTEMS IN WEB APPLICATIONS – THE TROI CASE

Avoiding digital marketing, surveys, reviews and online users behavior approaches on digital age are the key elements for a powerful businesses to fail, there are some systems that should preceded some artificial intelligence techniques. In this direction, the use of data mining for recommending relevant items as a new state of the art technique is increasing user satisfaction as well as the business revenues. And other related information gathering approaches in order to our systems thing and acts like humans. To do so there is a Recommender System that will be elaborated in this thesis. How people interact, how to calculate accurately and identify what people like or dislike based on their online previous behaviors. The thesis includes also the methodologies recommender system uses, how math equations helps Recommender Systems to calculate user’s behavior and similarities. The filters are important on Recommender System, explaining if similar users like the same product or item, which is the probability of neighbor user to like also. Here comes collaborative filters, neighborhood filters, hybrid recommender system with the use of various algorithms the Recommender Systems has the ability to predict whether a particular user would prefer an item or not, based on the user’s profile and their activities. The use of Recommender Systems are beneficial to both service providers and users. Thesis cover also the strength and weaknesses of Recommender Systems and how involving Ontology can improve it. Ontology-based methods can be used to reduce problems that content-based recommender systems are known to suffer from. Based on Kosovar’s GDP and youngsters job perspectives are desirable for improvements, the demand is greater than the offer. I thought of building an intelligence system that will be making easier for Kosovars to find the appropriate job that suits their profile, skills, knowledge, character and locations. And that system is called TROI Search engine that indexes and merge all local operating job seeking websites in one platform with intelligence features. Thesis will present the design, implementation, testing and evaluation of a TROI search engine. Testing is done by getting user experiments while using running environment of TROI search engine. Results show that the functionality of the recommender system is satisfactory and helpful