The importance of effective customer assistance technologies is imperative in today's online marketplace where users are oftentimes overwhelmed by the product choices availab...
John Doody, Edwin Costello, Lorraine McGinty, Barr...
Initial successes in the area of recommender systems have led to considerable early optimism. However as a research community, we are still in the early days of our understanding ...
- Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations based ...
The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-...
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
In the context of intelligent disclosure of case law, we report on our findings with respect to the presentation of relevant court decisions back to the laymen users. For this pres...
Gwen R. Wildeboer, Michel C. A. Klein, Elisabeth M...
Given the rapid growth of participatory media content such as blogs, there is a need to design personalized recommender systems to recommend only useful content to users. We belie...
Abstract: Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively litt...
Swapneel Sheth, Nipun Arora, Christian Murphy, Gai...
—User profiles derived from Web navigation data are used in important e-commerce applications such as Web personalization, recommender systems, and Web analytics. In the open en...
Recommender systems are widely used in E-Commerce for making automatic suggestions of new items that could meet the interest of a given user. Collaborative Filtering approaches co...