Top Research Papers on Recommender Systems

--Abundant information is extracted from web at every second. When queries are thrown to the search engine, they are generally contain few words and are in natural languages. The search engine is not able to identify natural language and thus it become difficult to extract correct information from world wide web based on the users interest. Here, the recommendation technique comes into picture. There are number of recommendation applications available in market which is used to support various kinds of data sources like text, images and videos. In this review we list out the approaches, techni...

This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.

Recommender systems give advice about products, information or services users might be interested in. They are intelligent applications to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services. And they are probably among the most prominent applications having a substantial impact on the performance of e-commerce sites and the sector in general. In fact, even if the problem of supporting a choice decision process is quite old, it is only with the advent of the WWW that we had at disposal, in a lar...

Recommender systems give advice about products, information or services users might be interested in to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services.

Recommender applications recommend everything from news, Web sites, CDs, books, and movies to more complex items such as financial services, digital cameras, or e-government services.

Recommender applications recommend everything from news, Web sites, CDs, books, and movies to more complex items such as financial services, digital cameras, or e-government services.

Recommender systems are web-based applications that aim at helping customers in the decision making and product selection process (Resnick & Varian, 1997). The most prominent example is the online bookstore amazon.com, where collaborative filtering techniques are used to exploit similarities in the user profile which is based on the navigation and buying history: The main idea is to identify users who presumably have similar preferences and recommend those items which were bought by other users with a similar interest profile. Another technical approach is content-based filtering which builds ...

In this review, the approaches, techniques and application of recommendation system are listed out which helps to map future direction of world wide web.

A hybrid recommender system for the movies ranking, where a movie based recommender system suggests the user about the movie that he should rank after performing the intelligent analysis using weighted approach.

Theoretical explanation and extensive systematic literature review on the topic of recommender systems are given.

It is shown that recommender systems connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data, a departure from the traditional content-based filtering versus collaborative design perspective.

It is shown that recommender systems connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data, which represents a departure from the traditional content-based filtering versus collaborative design perspective.

The purpose of this project is to use the theory of six degrees of separation amongst users of a social network to enhance existing recommender systems.

Research questions associated with recommender systems for TV and an example of such a recommender system for TV are presented and research questions related to such systems are presented.

Recommender systems have turn into an important recommendation technique on the web and are widely used for recommendation of different items. On web huge amount of data is available online, the need of analysis and personalization systems is increasing permanently. This paper presents introduction to the categories of recommender systems and different recommendation techniques are mainly classified into three categories: collaborative filtering, content based filtering and hybrid filtering. This paper also discusses challenges of recommender system. Every method has its weakness and strengths...

The long tail theory is introduced and its impact onRecommender systems and a comprehensive review of the different datasets used to evaluate collaborative filtering recommender systems techniques and algorithms is provided.

It is proposed that the recommender system should move beyond the conventional accuracy criteria and take some other criteria into account, such as coverage, diversity, serendipity, scalability, adaptability, risk, novelty and so on.

This thesis aims to distinguish metrics on recommender systems that can be proved useful to compare them, and performs a comparison between two algorithms of the collaborative filtering family.

This chapter (1) introduces recommender systems, classifying them along four dimensions and describing recent work done in the area and providing more details about one such type of recommender system, namely collaborative-recommendation systems.

This tutorial provides participants with a hands-on learning experience about using recommender system technologies, and they will understand the range of technologies being used for recommender systems, including collaborative filtering, rules-based systems, and information filtering.