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Recommending products to consumers means not only understanding their tastes,but also understanding their level of experience. For example, it would be amistake to recommend the iconic film Seven Samurai simply because a user enjoysother action movies; rather, we might conclude that they will eventually enjoyit -- 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 notbe the most `accessible'. Thus our goal in this paper is to recommend productsthat a user will enjoy now, while acknowledging that their tastes may havechanged over time, and may change again in the future. We model how tasteschange due to the very act of consuming more products -- in other words, asusers become more experienced. We develop a latent factor recommendation systemthat explicitly accounts for each user's level of experience. We find that sucha model not only leads to better recommendations, but also allows us to studythe role of user experience and expertise on a novel dataset of fifteen millionbeer, wine, food, and movie reviews.

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