Learning vector-space representations of items for recommendations using word embedding models

摘要

Learning vector-space representations of items for recommendations using word embedding models is described. In one or more embodiments, a word embedding model is used to produce item vector representations of items based on considering items interacted with as words and items interacted with during sessions as sentences. The item vectors are used to produce item recommendations similar to currently or recently viewed items.