Abstract
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Highlights
In everyday life, people rely on recommendations from other people by spoken words, reference letters, news reports from news media, general surveys, travel guides, and so forth
Applied to collaborative filtering (CF) tasks, working on real-world multiclass CF datasets and using Mean Absolute Error (MAE) as evaluation criterion, the empirical results show that the TAN-Extended logistic regression (ELR) CF and NBELR CF algorithms perform significantly better than the simple Bayesian CF algorithm, and consistently better than the Pearson correlation memory-based CF algorithm [11]
According to Herlocker et al [60], metrics evaluating recommendation systems can be broadly classified into the following broad categories: predictive accuracy metrics, such as Mean Absolute Error (MAE) and its variations; classification accuracy metrics, such as precision, recall, F1-measure, and ROC sensitivity; rank accuracy metrics, such as Pearson’s product-moment correlation, Kendall’s Tau, Mean Average Precision (MAP), half-life utility [9], and normalized distance-based performance metric (NDPM) [128]
Summary
People rely on recommendations from other people by spoken words, reference letters, news reports from news media, general surveys, travel guides, and so forth. We can convert the list of people and the movies they like or dislike (Table 1(a)) to a user-item ratings matrix (Table 1(b)), in which Tony is the active user that we want to make recommendations for. Generation collaborative filtering systems, such as GroupLens [5], use the user rating data to calculate the similarity or weight between users or items and make predictions or recommendations according to those calculated similarity values. The major difference between CF and content-based recommender systems is that CF only uses the user-item ratings data to make predictions and recommendations, while content-based recommender systems rely on the features of users and items for predictions [15].
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