Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Beh

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers’ interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers’ long-term stable preferences and evolutions into account.

近年のeコマースでは、消費者の振る舞いはリッチな情報含んでいる。嗜好のダイナミクスや消費習慣

existing workは、short term behaviorメインで, long term, evolutionは未考慮

In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users’ historical stable preferences and present consumption motivations.

Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning.

Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users.

Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.

NNベースの推薦手法

ユーザインタラクションに基づくアイテムの埋め込み

ターゲットユーザのモチベーションの学習

https://gyazo.com/3c52995b2d8a5894f8b85bc091845884

https://www.youtube.com/watch?v=GJDuBoDlMQw