Abstract These days, a recommendation of movie from server-based system has made finding piece cinema easier. Film helps us to find films that we need watch, instead searching extensively online and help cinephiles buffs by suggesting top tier watch without looking into huge databases which is very time consuming. As an approach this dilemma, Introduce model based on collaborative content-based...

In the e-commerce era, recommendation systems were introduced to share customer experience and comments. At the same time, there is a need for Ecommerce entities to join their recommender system databases to enhance the reliability toward prospective customers and also to maximize the precision of target marketing. However, there will be a privacy disclosure hazard while joining recommender sys...

In requirements engineering for recommender systems, software engineers must identify the data that drives the recommendations. This is a labor-intensive task, which is errorprone and expensive. One possible solution to this problem is the adoption of automatic recommender system development approach based on a general recommender framework. One step towards the creation of such a framework is ...

The Internet provides large varieties of content, which renders consumption difficult for users. However, recommender systems filter and personalize content according to individual preferences and deliver solutions that take the problem of information overload into account. Previous studies show different approaches to classify existing recommender technologies. Nevertheless, these do not yet i...

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data and generate recommendations by processing them un...

One of the most important class of Data Mining applications is the so called “Web Mining Systems” which analyzes and extracts important and non-trivial knowledge from Web related data. Typical applications of Web Mining are represented by the personalization or recommender systems. These systems are aimed to extract knowledge from the analysis of historical information of a web server in order ...

Recommender Systems are effective methods that have been developed successfully in e-commerce to provide personal recommendations based on user’s previous preferences. A recommender system assists users in a decision-making process to suggest interesting items from overwhelming flood of data available in the system. Suitably adapting the technology, recommender systems for medical purposes have...