Paper Notes: Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommenda

最新推荐文章于 2024-10-31 08:45:00 发布

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最新推荐文章于 2024-10-31 08:45:00 发布 · 880 阅读

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#深度学习 #神经网络 #数据挖掘 #机器学习

该论文提出了一种改进的传统RNN结构，通过时间感知控制器和内容感知控制器，考虑上下文信息以控制状态转换。此外，提出了一种基于注意力的框架，动态结合用户的长期和短期偏好，以适应具体上下文生成用户表示。实验显示，该方法在公共和工业数据集上优于多种最新方法。

Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation

LINK: https://doi.org/10.24963/ijcai.2019/585
CLASSIFICATION: RECOMMENDER-SYSTEM, SEQUENTIAL RECOMMENDER, LONG SHORT INTEREST
YEAR: Submitted on 24 May 2019
FROM: IJCAI 2019
WHAT PROBLEM TO SOLVE: Previous approaches neglect the importance of dynamically integrating long-term and short-term user modeling paradigms. Moreover, users’ behaviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling.
SOLUTION: In this paper, we improve the traditional RNN structure by proposing a time-aware controller and a content-aware controller, so that contextual information can be well considered to control the state transition. We further propose an attention-based framework to combine users’ long-term and short-term preferences, thus users’ representation can be generated adaptively according to the specific context. We conduct extensive experiments on both public and industrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.
CORE POINT:
- Two Key Problems
  1. Dynamic Time Intervals: Intuitively, two actions within a short time interval tend to share a closer relationship than two actions that within a long time interval. Thus this kind of temporal distance deserve special handling.
  2. Dynamic Latent Intent: Customer intent, also known as the user’s main purpose behind his/her behavior, is often changing from session to session. Irrelevant actions are useless for predicting a user’s certain future action.
- Short-Te