HYBRID RECOMMENDER SYSTEMS, SURVEY AND EXPERIMENTS newer ratings have the chance to tip the scales. Many adaptive systems include some sort of temporal discount to cause older ratings to have less influence, but they do so at the risk of losing information about interests that are long-term but sporadically exercised(Billsus Pazzani, 2000; Schwab et al., 2001). For example, a user might like to read about major earthquakes when they happen, but such occurrences are sufficiently rare that the ratings associated with last year's earthquake are gone by the time the next big one hits. Knowledge- and utility-based recommenders respond to the user's immediate need and do not need any kind of retraining when The ramp-up problem has the side-effect of excluding casual users from receiving the full benefits of colla borative and content-based recommendation. It is possible to do simple market-basket recommendation with minimal user input Amazon.com's'peoplewhoboughtXalsoboughtYbutthismechanismhas few of the advantages commonly associated with the collaborative filtering concept The learning-based technologies work best for dedicated users who are willing to invest some time making their preferences known to the system. Utility-and knowledge-based systems have fewer problems in this regard because they do not rely on having historical data about a user's preferences. Utility-based systems may present difficulties for casual users who might be unwilling to tailor a utility function simply to browse a catalog 3. Hybrid recommender systems Hybrid recommender systems combine two or more recommendation techniques to gain better performance with fewer of the drawbacks of any individual one. Most commonly, collaborative filtering is combined with some other technique in an met pt to avoid the ramp-up problem. Table IIT shows some of the combination 3.1. WEIGHTED A weighted hybrid recommender is one in which the score of a recommended item is computed from the results of all of the available recommendation techniques present in the system. For example, the simplest combined hybrid would be a linear com- bination of recommendation scores. The P-Tango system(Claypool et al., 1999) uses such a hybrid. It initially gives collaborative and content-based recommenders equal weight, but gradually adjusts the weighting as predictions about user ratings are confirmed or disconfirmed. Pazzani,s combination hybrid does not use numeric scores, but rather treats the output of each recommender(collaborative, con- tent-based and demographic)as a set of votes, which are then combined in a con sensus scheme(Pazzani, 1999 The benefit of a weighted hybrid is that all of the system's capabilities are brought to bear on the recommendation process in a straightforward way and it is easy to Reproduced with permission of the copyright owner. Further reproduction prohibited without permissionReproduced with permission of the copyright owner. Further reproduction prohibited without permission