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Title
Abstract
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Related Work 2.1 Pairwise Preferences
Experimental Strategy
Results
Perceived Recommendation Ality
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Data Science

Eliciting pairwise preferences in recommender systems

Profile image of Francesco RicciFrancesco Ricci

2018, Proceedings of the 12th ACM Conference on Recommender Systems

https://doi.org/10.1145/3240323.3240364
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Abstract

Preference data in the form of ratings or likes for items are widely used in many Recommender Systems. However, previous research has shown that even item comparisons, which generate pairwise preference data, can be used to model user preferences. Moreover, pairwise preferences can be e ectively combined with ratings to compute recommendations. In such hybrid approaches, the Recommender System requires to elicit both types of preference data from the user. In this work, we aim at identifying how and when to elicit pairwise preferences, i.e., when this form of user preference data is more meaningful for the user to express and more bene cial for the system. We conducted an online A/B test and compared a rating-only based system variant with another variant that allows the user to enter both types of preferences. Our results demonstrate that pairwise preferences are valuable and useful, especially when the user is focusing on a speci c type of items. By incorporating pairwise preferences, the system can generate be er recommendations than a state of the art rating-only based solution. Additionally, our results indicate that there seems to be a dependency between the user's personality, the perceived system usability and the satisfaction for the preference elicitation procedure, which varies if only ratings or a combination of ratings and pairwise preferences are elicited.

Figures (8)
Figure 1: Rating Interface
Figure 1: Rating Interface
Table 6: Comparison of PE scores between the two groups of users having low and high scores for the five FIPI personality traits (an asterisk means p < 0.05). The two systems, STS-R and STS-RC, are considered separately. Table 7: Comparison of SUS scores between the two groups of users having low and high scores for the five FIPI personality traits (an asterisk means p < 0.05). The two systems, STS-R and STS-RC, are considered separately.
Table 6: Comparison of PE scores between the two groups of users having low and high scores for the five FIPI personality traits (an asterisk means p < 0.05). The two systems, STS-R and STS-RC, are considered separately. Table 7: Comparison of SUS scores between the two groups of users having low and high scores for the five FIPI personality traits (an asterisk means p < 0.05). The two systems, STS-R and STS-RC, are considered separately.
providing an empirical confirmation that RSs based on pairwise preferences offer a valid alternative to traditional systems based on ratings. for pairwise preference elicitation, novel ranking algorithm and a specifically designed GUI.
providing an empirical confirmation that RSs based on pairwise preferences offer a valid alternative to traditional systems based on ratings. for pairwise preference elicitation, novel ranking algorithm and a specifically designed GUI.

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