Sequences, Items And Latent Links: Recommendation With Consumed Item Packs
This addresses the challenge of collecting explicit feedback for recommender systems, offering a generic approach for web content personalization, though it appears incremental as it builds on existing implicit feedback techniques.
The paper tackles the problem of recommendation using implicit feedback by introducing consumed item packs (CIP) to link users or items based on consumption behavior, resulting in three novel recommenders (CIP-U, CIP-I, DEEPCIP) that achieve competitive quality with state-of-the-art methods.
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-of-the-art 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 state-of-the-art ones, including one incorporating both explicit and implicit feedback.