Short-Lived Item Recommendation

Abstract

In highly dynamic markets (e.g., online news, short videos, and flash sales), new items continuously flow into the markets and fade out quickly. Also, users' preferences shift as they interact with a fast-evolving item pool. These two characteristics of short-lived items make personalized recommendation an essential but challenging task. The absence of user-item interactions for new items brings in the cold-start problem. Recommender systems also need to promptly update user and item representations to incorporate new interactions, so that the systems immediately yield improved recommendations for users throughout the short life-circle of items. To address these challenges, we propose Attention Initialized Dual Recurrent Neural Network (AI-DRNN), a holistic deep learning model that learns effective initial representations of new items via Attention Mechanism, and efficiently updates users/items representations via Dual Recurrent Neural Network to improve recommendation accuracy. We evaluate the proposed model with clickstream data from an online flash sale platform and report its performance in predicting consumer behavior regarding which product a consumer will interact with next and the type of interaction (placing an order or continuing to search). Empirical experiments show that the proposed AI-DRNN performs significantly better in prediction accuracy on cold-started items and overall, compared with the state-of-the-art benchmarks. We further explore the item representations to understand the dynamic updating process of AI-DRNN. We find that AI-DRNN demonstrates interpretability regarding how the algorithm works. Interestingly, we find the proposed AI-DRNN shares the key property of collaborative filtering theory (i.e., predicting a user’s interests by pooling preference information from other users).

Keywords: Short-Lived Item Recommendation; Cold Start; Recommender Systems; Dual Recurrent Neural Network; Deep Neural Network; Flash Sales; E-commerce