Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Recommender systems are software applications that help users to find items of interest in
situations of information overload. Current research often assumes a one-shot interaction …

This chapter aims to provide an overview of the class of multi-criteria recommender systems.
First, it defines the recommendation problem as a multicriteria decision making (MCDM) …

Critiquing-based recommender systems elicit users' feedback, called critiques, which they
made on the recommended items. This conversational style of interaction is in contract to the …

Conversational recommender systems aim to interactively support online users in their
information search and decision-making processes in an intuitive way. With the latest …

Objectives: Today the recommendation technology has managed to achieve a distinct place
in the modern and fascinating world of e-commerce applications as it helps the user in …

Recommender systems form an extremely diverse body of technologies and approaches.
The chapter aims to assist researchers and developers to identify the recommendation …

Recommender systems (RS) are vital for managing information overload and delivering
personalized content, responding to users' diverse information needs. The emergence of …

In conversational recommender systems (CRSs), conversations usually involve a set of
items and item-related entities or attributes, eg, director is a related entity of a movie. These …

In a recommender system that suggests options based on user attribute weights, the method
of preference elicitation (PE) employed by a recommender system can influence users' …