Personality, User Preferences, and Behavior in Recommender Systems

What is it about?

This paper reports on a study of the relationship between personality (using Big-five traits) and user retention and engagement, content preferences, and rating patterns for 1840 users in a recommender-system called MovieLens. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, the intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.

Featured Image

Photo by Sanket Kumar on Unsplash

Photo by Sanket Kumar on Unsplash

Why is it important?

Recommender systems often lack the data they need to make the best possible recommendations to their users. Especially during the cold-start phase, algorithmic approaches for making recommendations face serious challenges because they lack sufficient (and diverse) information about user-item preferences. Moreover, recommender systems do not consider the varying tastes of users although they may capture user behavior. Furthermore, prior work suggests that mere improvements in accuracy do not necessarily provide better user experience, and that, more user-centric approaches should be considered for improving the experience of users in recommender systems. We, therefore, explore the value of personality in this work

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