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Information Systems Frontiers

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13-09-2017

Personality, User Preferences and Behavior in Recommender systems

Authors: Raghav Pavan Karumur, Tien T. Nguyen, Joseph A. Konstan

Published in: Information Systems Frontiers | Issue 6/2018

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Abstract

This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.

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We choose these sessions to be consistent with prior work (Karumur et al. 2016b)

First session in MovieLens is substantially different from other sessions since a majority of ratings are provided by most users in this session.

By level of activity, we mean the number of ratings, the number of tags applied, the number of items a user adds to their wishlist, proportion of tags to ratings, number of pageviews, number of trailers viewed, extent to which trailers are viewed and so forth.

We do not state all possible hypothesis combinations for every personality type as we do not have prior knowledge on their nature that suggests an expected behavior from them for certain actions in MovieLens.

Trailers were a more recent feature on MovieLens. Only 401/1840 users used this feature. The results reported here, though statistically significant, are on small sample sizes for the low and high types. We provide preliminary results as trend evidence to guide future research.

Per Table 4, in some of the traits we have only about 50 users on one of the sides (low or high). We chose an equal number for its counterpart as well. For instance, Agreeableness has only 65 on the ‘low’ side. We therefore pick a random sample of 50 from both the low and high sides of Agreeableness. On the other hand, a trait like Extroversion has more than 100 on both the ‘low’ and ‘high’ sides and we pick equal samples of 100 each from both sides.

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Title: Personality, User Preferences and Behavior in Recommender systems
Authors: Raghav Pavan Karumur
Tien T. Nguyen
Joseph A. Konstan
Publication date: 13-09-2017
Publisher: Springer US
Published in: Information Systems Frontiers / Issue 6/2018
Print ISSN: 1387-3326
Electronic ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-017-9800-0

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