A Recommender system based on Java.
Recommender systems are a hot topic in this age of immense data and web marketing. Shopping online is ubiquitous, but online stores, while eminently searchable, lack the same browsing options as the brick-and-mortar variety. Visiting a bookstore in person, a customer can wander over to the science fiction section and casually look around without a particular author or title in mind. Online stores often offer a browsing option, and even allow browsing by genre, but often the number of options available is still overwhelming. Commercial sites try to counteract this overload by showing special deals, new options, and staff favorites, but the best marketing angle would be to recommend items that the user is likely to enjoy or need. Unless online stores want to hire psychics, they need a new technology. The field of data mining has a developing field of research in recommender systems, which fits the bill. Recommender systems are systems that, based on information about a user's past patterns and consumption patterns in general, recommend new items to the user. Some systems incorporate information about the items in question, others are based only on usage patterns; the latter kind of system is known as a collaborative filtering system. Instead of asking the user to explicitly pick filters for a search, collaborative filtering uses information about the user's past behavior and similar users to make suggestions
Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item.
Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services, life insurance, persons (online dating), and Twitter followers.