Evaluating Collaborative Filtering Recommender Algorithms: A Survey

A survey of collaborative filtering based social recommender systems

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of Collaborative Filtering(CF) based social recommender systems. We provide ...

A survey of active learning in collaborative filtering recommender systems

A Survey on Recommender Systems based on Collaborative Filtering Technique

Nowadays Product advertisement and viewer’s choice are two important part of marketing. These two parts generate a system that system is known as Recommender system. Recommender system plays a vital role in internet technology for data gathering and rating up a data. There are four types of filtering technique used in Recommender System-demographic, content, collaborative and hybrid. The most w...

Collaborative Filtering Recommender Systems

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the c...

Evaluating the Relative Performance of Collaborative Filtering Recommender Systems

Past work on the evaluation of recommender systems indicates that collaborative filtering algorithms are accurate and suitable for the top-N recommendation task. Further, the importance of performance beyond accuracy has been recognised in the literature. Here, we present an evaluation framework based on a set of accuracy and beyond accuracy metrics, including a novel metric that captures the u...