Advanced Recommender Systems

Introduction

It is increasingly difficult to find the right information on the Web in the age of explosive information overload. Recommender systems provide users with personalized suggestions of goods, services, or information and thus help them find the most relevant and interesting goods, services, or information for them. Over the last two decades since the first major recommender systems emerged in the mid-1990s (Konstan et al., 1997; Resnick & Varian, 1997), numerous recommender systems have been developed and used in various application domains including e-commerce, education, and engineering (Aggarwal, 2016; Jannach, Zanker, Felfernig, & Friedrich, 2011; Manouselis, Drachsler, Verbert, & Santos, 2014; Ricci, Rokach, & Shapira, 2015; Robillard, Maalej, Walker, & Zimmermann, 2014). Recommender systems have also proven very useful in various application domains.

A basic personalized recommender system suggests a list of items that seem to be most relevant for a given single target user without considering the context that the user is in by using users’ ratings of items on a single overall criterion where both users and items are in a single domain (Jannach et al., 2011). The basic recommender system can be extended in several ways. There are four major extensions, i.e., suggesting items for a group of target users rather than a single user (group recommendations), suggesting items by considering a specific context of the target user (context-aware recommendations), suggesting items using ratings on multiple criteria rather than a single overall criterion (multi-criteria recommendations), and suggesting items by using users and items in multiple domains rather than a single domain (multiple-domain recommendations).

In this chapter, we present a brief and systematic overview of four major advanced recommender systems — group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. We characterize and compare them within a unifying model as extensions of the basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed.