从业余爱好者到鉴赏家：通过在线评论建模用户专业知识的演变
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
摘要：
向消费者推荐产品不仅意味着了解他们的口味，还意味着了解他们的体验水平。例如，仅仅因为用户喜欢其他动作片而推荐标志性电影《七武士》是错误的;相反，我们可能会得出结论，他们最终会喜欢它--一旦他们准备好了。对于啤酒、葡萄酒、美食或任何用户已经形成口味的产品也是如此：“最好的”产品可能不是最“容易获得的”。因此，我们在本文中的目标是推荐用户现在喜欢的产品，同时承认他们的口味可能会随着时间的推移而改变，并且可能在未来再次改变。我们模拟了消费更多产品的行为如何改变口味-换句话说，随着用户变得更有经验。我们开发了一个潜在的因素推荐系统，明确占每个用户的经验水平。我们发现，这样的模型不仅可以带来更好的推荐，还可以让我们在一个包含1500万条啤酒、葡萄酒、食品和电影评论的新数据集上研究用户体验和专业知识的作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
