Network-based recommendation algorithms for user–object link predictions have achieved significant developments in recent years. For bipartite graphs, the resource reallocation in such algorithms is analogous to heat spreading (HeatS) or probability spreading (ProbS) processes. The best...

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented...

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid...

We study the online contagion of exogenous demand shocks generated by book reviews featured on the Oprah Winfrey TV show and published in the New York Times, through the co-purchase recommendation network on Amazon.com. These exogenous events may ripple through and affect the demand for a...

The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on...

Traditionally, recommender systems present recommendations in lists to the user. In content- and knowledge-based recommendation systems these list are often sorted on some notion of similarity with a query, ideal product specification, or sample product. However, a lot of information is lost in...