nowadays people are used to shopping online. They are used to the system, "guess you like it", and sometimes it seems to know you better than yourself. How exactly does the recommender system "guess" your mind,
(/Joseph, A., Konstan, &, John, Riedl) now, online shoppers have become accustomed to receiving personalized recommendations from the system. Netflix will recommend videos you might like to watch. TiVo will automatically list the sections, if you’re interested, you can look at them. Pandora generates personalized music streams by predicting what songs we want to listen to.
all of these recommendations come from a variety of recommender systems. They operate on computer algorithms, and serve customers according to customers’ browsing, searching, ordering, and preferences for customers to choose what they might like and possibly buy. The recommender system was designed to help online retailers increase sales, which is now a huge and growing business. At the same time, the recommendation system has developed from the last century, the mid 90s only dozens of research, development today has hundreds of researchers and other enterprises respectively at various universities, large online retailers and dozens of focus on this kind of system.
has made considerable progress in recommender systems over the years. At the beginning of their relatively rough, often make inaccurate predictions of behavior; but with more and different types of web user data becomes available, the recommended system will be innovative algorithms applied to these data, they rapidly improved. Today, recommender systems are extremely complex and sophisticated systems that often seem to know you better than yourself. At the same time, expand the recommendation system is to retail sites outside of the field: use them to guide the university students, the Cell Phone Corps rely on them to predict what users are likely to switch to another supplier, the conference organizers also tested with their assigned papers to reviewers.
the two of us have been developing and studying them since the early days of the recommender system, and initially participated in the GroupLens project (GroupLens Project) as an academic researcher. Since 1992, GroupLens has been sorting through information about Usenet forums in the American interest forum, pointing users to topics they may be interested in but have not yet discovered. A few years later, we founded Net Perceptions, a recommendation algorithm company that has been in the industry’s lead during the first boom in the Internet (1997 – 2000). In view of this, although these companies rarely talk publicly about how their recommender systems work, our