The basis for this personalization method is the assumption that persons with similar tastes will also have similar interests. First, "mentors" are used to deliver explicit ratings of website content and/or of products. When a visitor accesses the website, the information about him, his interests and his behavior is compared with that of the mentors (neighborhood analysis). Recommendations are then delivered based on this. Because this procedure is very complicated and time may be of the essence, implicit ratings (shopping cart, etc.) are also included in behavior analysis, which accordingly lowers the quality of the ratings.
A feature of CRM software that allows a business to provide products or services to a customer based on what other customers with similar preferences have purchased in the past. Internet retailers use collaborative filtering to recommend popular products to you.
Personalization technology that employs recommendation engines that use advanced statistical models and other forms of intelligent software to extract trends from the behavior of Web site visitors.
A CRM software feature whereby a company can offer products or services to a customer based on what other customers with similar preferences have purchased previously. Amazon.com uses collaborative filtering to recommend similar popular books to you.
Also known as "social filtering". A technique used to improve relevance , it returns documents other users with similar queries found relevant. This technique is also very effective in cross selling, as seen at Amazon.com ("People who bought 'Mary's Guide to Fast Food' also bought 'Jane's Recipes' ")
personalisation technology that uses recommendation engines to extract trends from the behaviour of website visitors and use that information to present suggestions to searchers. Amazon.com uses collaborative filtering to recommend books on the basis of purchases by other people with apparently similar interests.
Also called Community Knowledge, this is essentially a matching engine. It allows a company to serve up products or services to a particular customer based on what other customers with similar tastes or preferences have preferred in this particular product or service. For example, Amazon.com uses collaborative filtering to recommend books to you that were read by people with similar interests.
Selecting content based on the preferences of people with similar interests.
Example: Amazon tells me that other people who like the books I like are buying a particular book.
Collaborative filtering (CF) is the method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of CF approach is that: Those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for music tastes could make predictions about which music a should like given a partial list of that user's tastes (likes or dislikes).