1. Recommender systems and recommendation methods
Definitions of recommender systems can be found in different literature sources as follows:
--Any system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options (Burke 2002).
--Recommender system provides users with a ranked list of the recommended items (Herlocker et al. 2004).
--[...] people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients (Resnick, Varian 1997).
--Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics, and seeks to predict the 'rating' that a user would give to an item they had not yet considered. These characteristics may be from the information item (the content-based approach) or the user's social environment (Recommender systems 2011).
Adomavicius and Tuzhilin (2005) present an overview of the field of recommender systems and describe the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. Adomavicius and Tuzhilin (2005) also describe various limitations of current recommendation methods and discuss possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multi-criteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Recommender systems have been evaluated in many, often incomparable, ways. Herlocker et al. (2004) review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, Herlocker et al. (2004) present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.
Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders (Burke 2002). Burke (2002) surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Burke (2002) shows that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
Sarwar et al. (2000) investigate the use of dimensionality reduction to improve performance for a new class of data analysis software called "recommender systems". …