Automatic Conversational Recommender System for E-Commerce Platforms 电商领域的全自动对话推荐系统

Research Themes



Traditional product recommender systems often model user preferences based on historical information such as user inquiries and interactions with products. However, traditional systems cannot handle problems like unclear user demands and time-varying user preferences. In the conversational recommender system (CRS), the system obtains the explicit demands and dynamic preferences of users through real-time multi-turn interactions, so as to provide users with more accurate recommendation services. In recent years, many researches about CRS have been carried out. However, to apply CRS to real-world e-commerce platforms, there are still many challenges to overcome:

At first, the construction of CRS relies on the mining of product attributes and sales strategies of experts. However, existing automatic strategy mining algorithms usually produce results with coarse granularity and low accuracy. Such results cannot be directly used in practical applications and still requires complicated annotations. In addition, existing methods are not domain adaptive. When facing new dialogue scenarios or products from new categories, the mining system has to be reconstructed.

Secondly, the researches about conversation strategy module focus more on the interactive strategies aimed at product recommendation. These strategy modules cannot meet the requirements of real-world e-commerce platforms which have a package of marketing applications such as complementary-product recommendation, coupon recommendation, and selling point notification. A good strategy module should not only consider the mutual influence of different marketing applications and select the correct trigger time, but also guide the conversation topics and make users feel comfortable in the interactions. In addition to the conversion rate, the strategy module should also consider whether the customer's problems have been solved and his rating to the customer service.

To sum up, there are still many valuable problems to be overcome in the automatic conversational recommender system for e-commerce platforms. We hope to build an efficient CRS without laborious annotation and high operating costs, which can provide customers with more convenient shopping experience and bring higher revenue to the sellers.






1.Automatic mining of product attributes from the dialogues in e-commerce platforms. Attribute and attribute value mining is a kind of aspect mining. Existing researches focus on mining from sentences or paragraphs. Mining attributes and values from dialogues in the scenario of e-commerce customer service not only needs to consider the dialogue context, but also faces challenges such as informal and fragmented queries. And considering the huge amount of products in Taobao, it is hoped that the semi-supervised mining method can be used to reduce the cost.

2.Multi-task conversational strategy module for conversion rate and satisfaction rate. By combining the cutting-edge reinforcement learning and multi-task learning methods, we seek to build a conversational strategy module which can not only improve the conversion rate, but also make customers feel comfortable in the interactions.

3.Attractive response generation. Traditional response generation methods focus on generating readable and accurate responses. We hope to generate more attractive answers by combining user preferences, product properties and dialogue context, so as to improve the conversion rate of inquiries while solving the user's questions.

4.Dynamic update for user representation in Conversational Recommender System. During the interactions with customers, the customer's preferences to product attributes are updated in real time. We seek to build a method which can update the customer's implicit representation dynamically, so as to provide more accurate product recommendation.

1.电商场景下面向对话交互的属性挖掘。属性和属性值挖掘任务属于Aspect Mining的一种,学术界的研究数据通常是基于句子或篇章维度。在电商客服场景下挖掘对话交互中的商品属性,不仅需要考虑上下文语境,同时面临着成分缺省、表述随意等挑战。此外由于淘系商品数量巨大,希望能够利用半监督挖掘方法降低挖掘成本。




Related Research Topics

  • 商品属性抽取 (Product Attribute Value Extraction)
  • 细粒度(属性)情感分析(Aspect-based Sentiment Analysis)
  • 对话结构挖掘 (Dialogue Structure Mining)
  • 探索与利用问题(Trade-offs between Exploration and Exploitation)
  • 对话决策的迁移技术(Transfer Learning for Dialogue Policy)
  • 交互话术生成(Multi-turn Script Generation)
  • 多目标对话策略学习(Multi-task Conversational Strategies Learning)
  • 对话推荐系统的评估与用户模拟(Evaluation and User simulation for CRS)

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