There is an increasing consensus in the Recommender Systems community that the
dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly …

Many e-commerce sites present additional item recommendations to their visitors while they
navigate the site, and ample evidence exists that such recommendations are valuable for …

Recommender systems are among the most visible applications of intelligent systems
technology in practice and are used to help users find items of interest, for example on e …

Academic research in the field of recommender systems mainly focuses on the problem of
maximizing the users' utility by trying to identify the most relevant items for each user …

Making session-based recommendations, ie, recommending items solely based on the
users' last interactions without having access to their long-term preference pro les, is a …

We develop a probabilistic formulation giving rise to a formal version of heuristic k nearest-
neighbor (kNN) collaborative filtering. Different independence assumptions in our scheme …

Modelling the temporal context efficiently and effectively is essential to provide useful
recommendations to users. Methods such as matrix factorisation and Markov chains have …

Popularity bias is a phenomenon associated with collaborative filtering algorithms, in which
popular items tend to be recommended over unpopular items. As the appropriate level of …

Neighbor-based collaborative ranking (NCR) techniques follow three consecutive steps to
recommend items to each target user: first they calculate the similarities among users, then …

This dissertation is about filtering and discovering information online while using
recommender systems. In the first part of our research, we study the phenomenon of …