Recommender system has been one of the main enabler of eCommerce personalization, and there have been many researches for improving the fitness of recommendation. However, the necessity of aggregated personal information has pointed out the privacy risk of recommender system. In this paper, we provide basic framework for analyzing the behavior of private information sharing in recommender syste...

Shilling Attack Prevention for Recommender Systems Using Social-based Clustering Tak Man Desmond Lee A Recommender System (RS) is a system that utilizes user and item information to predict the feeling of users towards unfamiliar items. Recommender Systems have become popular tools for online stores due to their usefulness in confidently recommending items to users. A popular algorithm for reco...

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. Userand item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are...

A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendat...

Trust has been explored by many researchers in the past as a solution for assisting the process of recommendation production. In this work we are examining the feasibility of building networks of trusted users using the existing evidence that would be provided by a standard recommender system. As there is lack of models today that could help in finding the relationship between trust and similar...

Trust has been shown as a crucial factor for the adoption of new technologies. Surprisingly, trust literature offers very little guidance for systematically integrating the vast amount of insights from behavioral research on trust into the development of computing systems. The aim of this article is to translate results from behavioral sciences into software requirement patterns that address us...

This PhD thesis addresses the following problem: exploiting of trust information in order to enhance the accuracy and the user acceptance of current Recommender Systems (RS). RSs suggest to users items they will probably like. Up to now, current RSs mainly generate recommendations based on users’ opinions on items. Nowadays, with the growth of online communities, emarketplaces, weblogs and peer...

Recommender systems that inform consumers about their likely ideal product have become the cornerstone of eCommerce platforms that sell products from competing manufacturers. Using a model of an electronic marketplace in which two competing manufacturers produce substitutable products and sell the products through a common retail platform, we study the e ect of recommender systems on the market...

Withtherapiddevelopmentofmobileinternet,itisdifficulttoobtainhigh-qualityrecommendationin suchacomplicatedmobileenvironment,justdependingontraditionaluser-itembinaryinformation. Howtousemultiplecontextstogeneratesatisfyingrecommendationhasbeenahottopicinsomefields likee-commerce,tourismandnews.Contextawarerecommendersystem(CARS)importsconte...

Recommender systems help customers to choose right product or service from large number of alternatives available on Internet. In recent time, trust becomes an important issue in designing effective recommender systems. In this paper we have studied the role of trust and distrust in designing recommender systems. General Terms E-Commerce, Information Retrieval, Web Mining.