Recommender system

A recommender system or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the 'rating' or 'preference' a user would give to an item. They are primarily used in commercial applications.Predictive accuracy is substantially improved when blending multiple predictors. Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique. Consequently, our solution is an ensemble of many methods. A recommender system or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the 'rating' or 'preference' a user would give to an item. They are primarily used in commercial applications. Recommender systems are utilized in a variety of areas, and are most commonly recognized as playlist generators for video and music services like Netflix, YouTube and Spotify, product recommenders for services such as Amazon, or content recommenders for social media platforms such as Facebook and Twitter. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, books, and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have been developed to explore research articles and experts, collaborators, financial services, and life insurance. Recommender systems usually make use of either or both collaborative filtering and content-based filtering (also known as the personality-based approach), as well as other systems such as knowledge-based systems. Collaborative filtering approaches build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties. Current recommender systems typically combine one or more approaches into a hybrid system. The differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems – Last.fm and Pandora Radio. Each type of system has its strengths and weaknesses. In the above example, Last.fm requires a large amount of information about a user to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems. Whereas Pandora needs very little information to start, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed). Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data. Recommender systems were first mentioned in a technical report as a 'digital bookshelf' in 1990 by Jussi Karlgren at Columbia University, and implemented at scale and worked through in technical reports and publications from 1994 onwards by Jussi Karlgren, then at SICS,and research groups led by Pattie Maes at MIT, Will Hill at Bellcore, and Paul Resnick, also at MITwhose work with GroupLens was awarded the 2010 ACM Software Systems Award. Montaner provided the first overview of recommender systems from an intelligent agent perspective. Adomavicius provided a new, alternate overview of recommender systems. Herlocker provides an additional overview of evaluation techniques for recommender systems, and Beel et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges. Recommender systems have been the focus of several granted patents.