Table 4: Summary of the similarity of user satisfaction perception among algorithms.
Question Algorithms considered similar
The system finds the furniture the user wants to view 1 and 2
The system filters out the furniture the user does not want 1, 2, and 3
The system captures the right category 1 and 2
The system captures the users interests 1, 2, and 3
The system finds interesting furniture efficiently 1 and 2
Overall satisfaction 1, 2, and 3
Nevertheless, this study still contributes to the litera-
ture on recommender systems evaluations that go be-
yond algorithmic accuracy, as claimed by (Konstan
and Riedl, 2012).
7 CONCLUSIONS
We presented the results of an experiment that aimed
at identifying how different recommendation algo-
rithms trigger different perceptions of satisfaction on
users. We tested three algorithms using the database
of a real furniture small-retailer.
Our results pointed out no significant difference in
user satisfaction regarding the compared algorithms.
Algorithms were found to be generally similar, al-
though some difference was observed in specific is-
sues. In this case, the algorithm which showed similar
products performed better.
Future studies should focus on including a
content-based algorithm in the experiment to be com-
pared with collaborative filtering algorithms. We also
plan to reduce our threat to validity, by including sat-
isfaction evaluation with on-line users in their real
context.
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