ZZZ_Background - GetRecced/IR670_Spring2018 GitHub Wiki

Recommender Systems mainly rely on human feedback in the form of ratings and reviews. Hence, the system suffers through the problem of cold start where new user or item does not have much of the feedback available. This makes the initial feedback a lot invaluable. Intuitively, one review gives a lot more information about the user compared to one rating.

In spite of the wealth of research on modeling ratings, the other form of feedback present on review websites—namely, the reviews themselves—is typically ignored. In our opinion, ignoring this rich source of information is a major shortcoming of existing work on recommender systems. Indeed, if our goal is to understand (rather than merely predict) how users rate products, we ought to rely on reviews, whose very purpose is for users to explain why they rated a product the way they did.^[1]