In the past, classic recommender systems relied solely on the user models they were able to construct by themselves and suffered from the “cold start” problem. Recent decade advances, among them internet connectivity and data sharing, now enable them to bootstrap their user models from external sources such as user modeling servers or other recommender systems. However, this approach has only b...

Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone as far as using implicit feedback indicators to understand users’ interests. More than a decade after the emergence of recommender systems, the question whether users prefer them compared to stating their preferences explicitl...

Profiling users’ temporal learning interests is key to online course recommendation. Previous studies mainly profile by aggregating their historical behaviors with simple fusing strategies, which fails capture interest patterns underlying the sequential user behaviors. To fill gap, we devise a recommender that incorporates time-aware Transformers and knowledge graph better interests. First, int...

regularly in e-commerce settings. A user, Jane, visits her favorite online bookstore. The homepage lists current best-sellers and also a list containing recommended items. This list might include, for example, a new book published by one of Jane’s favorite authors, a cookbook by a new author, and a supernatural thriller. Whether Jane will find these suggestions useful or distracting is a functi...

0020-0255/$ see front matter 2010 Elsevier Inc doi:10.1016/j.ins.2010.05.036 * Corresponding author. E-mail address: [email protected] (L. Zhen). A novel model of distributed knowledge recommender system is proposed to facilitate knowledge sharing among collaborative team members. Different from traditional recommender systems in the client–server architecture, our model is oriented to the peer-...

Constraint-based recommender applications provide valuable support in item selection processes related to complex products and services. This type of recommender operates on a knowledge base that contains a deep model of the product assortment as well as constraints representing the company’s marketing and sales rules. Due to changes in the product assortment as well as in marketing and sales r...

Recommender systems have evolved in the last years as specialized tools to assist users in a plethora of computermediated tasks by providing guidelines or hints. Most recommender systems are aimed at facilitating access to relevant items, a situation particularly common when performing web-based tasks. At the same time, defeasible argumentation has evolved as a successful approach in AI to mode...

One of the current challenges concerning improving recommender systems consists of finding ways of increasing serendipity and diversity, without compromising the precision and recall of the system. One possible way to approach this problem is to complement a standard recommender by another recommender “orthogonal” to the standard one, i.e. one that recommends different items than the standard. ...