Recommender systems: a way to client’s heart Recommender systems have become a mainstream in satisfying customers’ needs in recent years and are used for various applications whether movies, music, news, books or scientific articles etc. Let’s have an overlook on this newly found way to a client’s heart. Recommender systems are a subclass of information filtering system, removing unwanted information, that seek to predict the ‘rating’ or ‘preference’ that user would give to an item. Main goal: recommender systems attempt to present to the user information items (film, television, music, books, news, web pages) the user is really interested in. These systems typically produce a list of recommendations in one of two ways – through collaborative or content-based filtering. The motivation for collaborative filtering comes from the idea that people often get the best recommendations from someone with similar tastes to themselves. Collaborative filtering explores techniques for matching people with similar interests and making recommendations on this basis. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties. These approaches are often combined to Hybrid Recommender Systems. Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves. Interestingly enough, recommender systems are often implemented using search engines indexing non-traditional data. Facebook, MySpace, LinkedIn, and other social networks use collaborative filtering to recommend new friends, groups, and other social connections (by examining the network of connections between a user and their friends). So a new concept of personalization is at the scene now. It applies a recent trend of using the social network information to improve satisfaction of individual needs and meet customers’ interests. One of the aspects is not just archiving of the history of likes/dislikes/purchases but rather unique combination when an individuality interaction and engagement with society is taken into account. Some companies are ready for very generous steps to reach clients’ hearts. For example Netflix announced a special prize of 1 million dollars for the best collaborative filtering algorithm to predict user ratings for films and searched for talents through 2007-2009 years. Who else use recommender systems? - Amazon.com – one of the best examples of companies currently using such system of recommendations as well as Music Genome Project and other services from films to fantasy books. When for example a client logs in Facebook Connect inside Amazon.com, it uses profile data so that Amazon can offer the products in which a user may be interested. Compare this to the old way of doing things when Amazon made product offerings based on the fact that you have already purchased. But what if the user has purchased a book about golf for his brother and spirits for his wife? Recommendations based on this information wouldn’ t be yourself attraction. That all changes with a favorable count. Amazon can make sure that the first thing people look – the choice is deeply connected to their personal preferences. This is – a great way to keep users on the site, and increase the likelihood of purchase generate more revenue in the short-and long-term - Reddit and StumbleUpon created recommendation systems based on clients’ previous likes and dislikes to present them with most relevant information.
- COMPASS – a Content Discovery Platform-Social TV guide provides personalized recommendations for video and works on tablets and smartphones to create a more personalized user experience. - Kids’ apparel seller Step2 improved shopping experience by implementing ‘following’ option of tastes of a relevant for a client person with similar preferences by using their Facebook account. - Zyshopper developed application for mobile shopping based on emotional purchase to solve the problem of huge amount of choice of conventional online catalogues. Application concentrates on what person is mostly interested in. Future is in recommender industry-a business that will continue to grow and become more sophisticated uniting psychology, economics and cognitive science, providing information of value to an individual. Materials for this small analysis sourced from related articles of en.wikipedia.org -free encyclopedia. Thank you for reading.