  Performance of recommender systems (RS) relies heavily on the amount of
training data available. This poses a chicken-and-egg problem for early-stage
products, whose amount of data, in turn, relies on the performance of their RS.
On the other hand, zero-shot learning promises some degree of generalization
from an old dataset to an entirely new dataset. In this paper, we explore the
possibility of zero-shot learning in RS. We develop an algorithm, dubbed
ZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and
generalize to a new one where there are neither overlapping users nor
overlapping items, a setting that contrasts typical cross-domain RS that has
either overlapping users or items. Different from categorical item indices,
i.e., item ID, in previous methods, ZESRec uses items' natural-language
descriptions (or description embeddings) as their continuous indices, and
therefore naturally generalize to any unseen items. In terms of users, ZESRec
builds upon recent advances on sequential RS to represent users using their
interactions with items, thereby generalizing to unseen users as well. We study
three pairs of real-world RS datasets and demonstrate that ZESRec can
successfully enable recommendations in such a zero-shot setting, opening up new
opportunities for resolving the chicken-and-egg problem for data-scarce
startups or early-stage products.
