Paper Info
Title
User-Centric Vs. System-Centric Evaluation Of Recommender Systems
Abstract
Recommender Systems (RSs) aim at helping users search large amounts of contents and identify more effectively the items (products or services) that are likely to be more useful or attractive. The quality of a RS can be defined from two perspectives: system-centric, in which quality measures (e.g., precision, recall) are evaluated using vast datasets of preferences and opinions on items previously collected from users that are not interacting with the RS under study; user-centric, in which user measures are collected from users interacting with the RS under study. Prior research in e-commerce has provided some empirical evidence that system-centric and user-centric quality methods may lead to inconsistent results, e.g., RSs that were "best" according to systemcentric measures were not the top ones according to user-centric measures. The paper investigates if a similar mismatch also exists in the domain of e-tourism. We discuss two studies that have adopted a system-centric approach using data from 210000 users, and a user-centric approach involving 240 users interacting with an online hotel booking service. In both studies, we considered four RSs that employ an implicit user preference elicitation technique and different baseline and state-of-the-art recommendation algorithms. In these four experimental conditions, we compared system-centric quality measures against user-centric evaluation results. System-centric quality measures were consistent with user-centric measures, in contrast with past studies in e-commerce. This pinpoints that the relationship between the two kinds of metrics may depend on the business sector, is more complex that we may expect, and is a challenging issues that deserves further research.
Year
DOI
Venue
2013
10.1007/978-3-642-40477-1_21
HUMAN-COMPUTER INTERACTION - INTERACT 2013, PT III
Keywords
Field
DocType
Recommender systems, E-tourism, Evaluation, Decision Making
Recommender system,Preference elicitation,Empirical evidence,Computer science,Human–computer interaction,Business sector,RSS,Recall,User-centered design
Conference
Volume
ISSN
Citations 
8119
0302-9743
14
PageRank 
References 
Authors
0.80
26
3
Name
Order
Citations
PageRank
Paolo Cremonesi1130687.23
Franca Garzotto21245203.98
Roberto Turrin385934.94