Automated collaborative filtering (ACF) systems predict a person’s affinity for items or information by connecting that person’s recorded interests with the recorded interests...
Jonathan L. Herlocker, Joseph A. Konstan, John Rie...
Web-based applications with a large variety of users suffer from the inability to satisfy heterogeneous needs. A remedy for the negative effects of the traditional "one-size-...
Paolo Buono, Maria Francesca Costabile, Stefano Gu...
Collaborative filtering uses a database about consumers’ preferences to make personal product recommendations and is achieving widespread success in both E-Commerce and Informat...
Kai Yu, Xiaowei Xu, Martin Ester, Hans-Peter Krieg...
Current recommender systems, based on collaborative filtering, implement a rather limited model of interaction. These systems intelligently elicit information from a user only dur...
In this work, we apply a clustering technique to integrate the contents of items into the item-based collaborative filtering framework. The group rating information that is obtain...
There are many e-commerce applications on the web. A common shortcoming is the lack of customer service and marketing analysis tools in most ecommerce web sites. In order to overc...
Collaborative filtering and content-based filtering are two types of information filtering techniques. Combining these two techniques can improve the recommendation effectiveness....
Many collaborative music recommender systems (CMRS) have succeeded in capturing the similarity among users or items based on ratings, however they have rarely considered about the...
Qing Li, Byeong Man Kim, Donghai Guan, Duk whan Oh
The goal of collaborative filtering is to make recommendations for a test user by utilizing the rating information of users who share interests similar to the test user. Because r...
Collaborative filtering identifies information interest of a particular user based on the information provided by other similar users. The memory-based approaches for collaborativ...