In this paper, we propose a framework for computational serendipity. The framework is used
in a recommender system context to find personalized serendipity and meanwhile stimulate …

Serendipity is recognized as very challenging to simulate and stimulate in recommender
systems. In this paper, we adopt a novel approach to model and implement serendipity in a …

Recommender systems suggest items, such as movies or books, to users based on their
interests. These systems often suggest items that users are either already familiar with or …

Recommender systems use past behaviors of users to suggest items. Most tend to offer
items similar to the items that a target user has indicated as interesting. As a result, users …

In the digital era, personalisation systems are the typical way to deal with the massive
amount of information on the Web. ese systems decide in our place what we like, possibly …

Over the past several years, research in recommender systems has emphasized the
importance of serendipity, but there is still no consensus on the definition of this concept and …

A recommender system is employed to accurately recommend items, which are expected to
attract the user's attention. The over-emphasis on the accuracy of the recommendations can …

Recommender systems are nowadays widely implemented in order to predict the potential
objects of interest for the user. With the wide world of the internet, these systems are …

Most recommender systems suggest items to a user that are popular among all users and
similar to items the user usually consumes. As a result, a user receives recommendations …

Serendipity-oriented recommender systems have increasingly been recognized as useful to
overcome the" filter bubble" problem of accuracy-oriented recommenders, by recommending …