Comparison of collaborative filtering algorithms

Abstract

The technique of collaborative filtering is especially successful in generating personalized recommendations. More than a decade of research has resulted in numerous algorithms, although no comparison of the different strategies has been made. In fact, a universally accepted way of evaluating a collaborative filtering algorithm does not exist yet. In this work, we compare different techniques found in the literature, and we study the characteristics of each one, highlighting their principal strengths and weaknesses. Several experiments have been performed, using the most popular metrics and algorithms. Moreover, two new metrics designed to measure the precision on good items have been proposed. The results have revealed the weaknesses of many algorithms in extracting information from user profiles especially under sparsity conditions. We have also confirmed the good results of SVD-based techniques already reported by other authors. As an alternative, we present a new approach based on the interpretation of the tendencies or differences between users and items. Despite its extraordinary simplicity, in our experiments, it obtained noticeably better results than more complex algorithms. In fact, in the cases analyzed, its results are at least equivalent to those of the best approaches studied. Under sparsity conditions, there is more than a 20% improvement in accuracy over the traditional user-based algorithms, while maintaining over 90% coverage. Moreover, it is much more efficient computationally than any other algorithm, making it especially adequate for large amounts of data.

A Fast Recommender System for Cold User Using Categorized Items

“…In this study, the target users only include cold users. To find users similar to the cold user, there are many similarity measures [4,[12][13][14][15][16][17]. Among the best of them is the cosine similarity measure [35], which is widely used to measure the similarity between users in CF.…”

“…In fact, the core of CF is to find the similarities between users. CF is possible in two ways: using memory-based and model-based approaches [12]; the combination of these two methods is also used [13,14]. The memory-based CF is used in this study.…”

Solving Cold-Start Problem by Combining Personality Traits and Demographic Attributes in a User Based Recommender System

“…In the user-based CF algorithm, a subset of nearest neighbors of the target user are chosen based on their similarity with him or her, and a weighted aggregate of their ratings is used to generate predictions for the target user. In this paper, we use the modified method of plain user based prediction method to predict the rating of a target user.as shown below, for that purpose, the formula used in plain User-based Filtering [4], [ 6] was modified as follows:…”

“…(3) compute the prediction using the neighbor ratings [6], [20].  Data Sparsity problem arises if the user-item matrix containing ratings details is extremely sparse and this situation further leads to inefficient recommender systems which are based upon nearest-neighbor algorithms for calculating user similarity.…”

Balancing the popularity bias of object similarities for personalised recommendation

“…The recommender systems have got significant attentions and wide applications over the past decades due to its advances in finding users' potential interests [4][5][6]. Various algorithms have been developed including the content-based systems which applies the object information such as attributes [7], contents [8] and tags [9,10] to define similarities, and the most widely used collaborative filtering systems [1,11,12].…”