Sequences, Items And Latent Links: Recommendation With Consumed Item Packs

Abstract

Recommenders personalize the web content by typically using collaborative filtering to relate users (or items) based on explicit feedback, e.g., ratings. The difficulty of collecting this feedback has recently motivated to consider implicit feedback (e.g., item consumption along with the corresponding time).In this paper, we introduce the notion of consumed item pack (CIP) which enables to link users (or items) based on their implicit analogous consumption behavior. Our proposal is generic, and we show that it captures three novel implicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a word embedding-based (DEEPCIP), as well as a state-ofart technique using implicit feedback (FISM). We show that our recommenders handle incremental updates incorporating freshly consumed items. We demonstrate that all three recommenders provide a recommendation quality that is competitive with stateof-the-art ones, including one incorporating both explicit and implicit feedback.

BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations

“…Likewise, individuals gradually change their taste in movies and music. There have been also several work on incorporating such temporality in recommendations [16,6,12,9]. Demography.…”