1. ABSTRACT Human beings, not machines, are the ultimate experts for information retrieval tasks, including recommender systems. Consequently, computers are most useful when they combine information about people’s judgments. Collaborative filtering systems make use of this observation by having users explicitly rate items, such as Web pages, with the system making recommendations to other users...

Recommender Systems (RSs) are garnering a significant importance with the advent of e-commerce and ebusiness on the web. This paper focused on the Movie Recommender System (MRS) based on human emotions. The problem is the MRS need to capture exactly the customer’s profile and features of movies, therefore movie is a complex domain and emotions is a human interaction domain, so difficult to comb...

This paper presents analytical outcomes of scientometric mapping of research work done on the important emerging area of ‘Recommender Systems. Research on ‘Recommender Systems’ started during last few years and within a short span of time has gained tremendous momentum. It is now considered as important emerging areas of research in computational sciences and related disciplines. We have analyz...

The widespread adoption of smartphones is now putting both the Internet and sensor-rich hardware into the pockets of millions. While recommender systems have become the norm on many web sites, many mobile systems have historically been built as location-based services. However, these devices are becoming the ideal interface for recommender systems that help users discover, explore, and learn ab...

In recent years recommender systems (RSs) has gained popularity to solve the problem of web information overload and redundancy. Recommendation system helps users in finding the contents of their interest with minimum efforts. Even though most of the systems use explicit rating to recommend the content of users interest. When reading the electronic books performance of user gets affected becaus...

In the past few years, we have developed a research paper recommender system for our reference management software Docear. In this paper, we introduce the architecture of the recommender system and four datasets. The architecture comprises of multiple components, e.g. for crawling PDFs, generating user models, and calculating content‐based recommendations. It supports researchers and developers...

The aim of Recommender Systems is to suggest useful items to users. Three major techniques can be highlighted in these systems: Collaborative Filtering, Content-Based Filtering and Hybrid Filtering. The collaborative method proposes recommendations based on what a group of users have enjoyed and it is widely used in Open Source Recommender Systems. The work presented in this paper takes place i...

This paper proposes the design of a recommender system that uses knowledge stored in the form of ontologies. The interactions amongst the peer agents for generating recommendations are based on the trust network that exists between them. Recommendations about a product given by peer agents are in the form of Intuitionistic Fuzzy Sets specified using degree of membership, non membership and unce...

Recommender systems have become an important personalization technique on the web and are widely used especially in e-commerce applications. However, operators of web shops and other platforms are challenged by the large variety of available algorithms and the multitude of their possible parameterizations. Since the quality of the recommendations that are given can have a significant business i...

Homeland security intelligence analysts need help finding relevant information quickly in a rapidly increasing volume of incoming raw data. Many different AI techniques are needed to handle this deluge of data. This paper describes initial investigations in the application of recommender systems to this problem. It illustrates various recommender systems technologies and suggests scenarios for ...