From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews

TL;DR

This paper introduces a recommendation model that tracks and predicts changes in user tastes as they gain experience over time, improving personalized suggestions for products like movies, foods, and beverages.

Contribution

It presents a novel latent factor recommendation system that explicitly models user experience levels and their evolution, enhancing recommendation accuracy and understanding of taste development.

Findings

Model improves recommendation quality over traditional methods.

User experience significantly influences taste preferences.

The approach provides insights into how tastes evolve with consumption.

Abstract

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the `best' products may not be the most `accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.