The paper titled "Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate" addresses the challenge of accurately estimating post-click conversion rate (CVR) in industrial applications such as recommendation and advertising. While conventional CVR modeling techniques based on deep learning methods have achieved state-of-the-art performance, they face several task-specific problems in practice. One of the main challenges is the sample selection bias problem. Conventional CVR models are trained using samples of clicked impressions but are then used to make inferences on the entire space with samples of all impressions. This mismatch between training and inference data causes a bias in the model's performance. Another challenge is the extreme data sparsity problem. The scarcity of data makes it difficult to fit the CVR model effectively, leading to suboptimal performance. To address these challenges, the authors propose a brand-new perspective for modeling CVR by leveraging the sequential pattern of user actions: impression -> click -> conversion. They introduce the Entire Space Multi-task Model (ESMM), which directly models CVR over the entire space and employs a feature representation transfer learning strategy. The ESMM approach simultaneously eliminates both the sample selection bias problem and the extreme data sparsity problem. By modeling CVR directly over the entire space, ESMM ensures that inferences are made using samples representative of all impressions. Additionally, employing a feature representation transfer learning strategy helps improve model fitting even with limited data. Experiments conducted on a dataset gathered from Taobao's recommender system demonstrate that ESMM outperforms competitive methods significantly. The authors also release a sampling version of this dataset to facilitate future research. Overall, this paper presents an innovative approach to CVR modeling that tackles key challenges faced by conventional methods and provides valuable insights into improving ranking systems in industrial applications such as recommendation and advertising.
The paper talks about a problem in estimating how many people buy things after clicking on ads or recommendations. The usual ways of figuring this out have some problems. The authors suggest a new way called ESMM that looks at the order of what people do - like seeing an ad, clicking on it, and then buying something. ESMM can look at all the data and fix the problems with the old ways. They tested ESMM on a big dataset from Taobao and it worked better than other methods. The authors also made part of the dataset available for other researchers to use.
Definitions
- Estimating: Trying to figure out or guess something.
- Conversion rate (CVR): How many people buy something after clicking on an ad or recommendation.
- Industrial applications: Using something in real-life situations like selling products or showing ads.
- Sequential pattern: The order in which things happen one after another.
- Feature representation transfer learning strategy: A way of using information from one thing to help with another thing.
- Dataset: A collection of information that can be studied or analyzed.
Overview of Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Estimating post-click conversion rate (CVR) is a critical challenge in industrial applications such as recommendation and advertising. While conventional CVR modeling techniques based on deep learning methods have achieved state-of-the-art performance, they face several task-specific problems in practice. To address these challenges, the authors propose a brand new perspective for modeling CVR by leveraging the sequential pattern of user actions: impression -> click -> conversion. They introduce the Entire Space Multi-task Model (ESMM), which directly models CVR over the entire space and employs a feature representation transfer learning strategy to improve model fitting even with limited data. Experiments conducted on a dataset gathered from Taobao's recommender system demonstrate that ESMM outperforms competitive methods significantly, providing valuable insights into improving ranking systems in industrial applications such as recommendation and advertising.
Challenges Faced by Conventional CVR Models
Conventional CVR models are trained using samples of clicked impressions but are then used to make inferences on the entire space with samples of all impressions. This mismatch between training and inference data causes a sample selection bias problem, leading to suboptimal performance due to inaccurate predictions about nonclicked items. Additionally, extreme data sparsity makes it difficult to fit the CVR model effectively, further reducing its accuracy.
The Entire Space Multi-task Model (ESMM)
To tackle these challenges, the authors propose an innovative approach called ESMM that directly models CVR over the entire space instead of relying on samples from only clicked impressions or nonclicked impressions separately. By modeling CVR directly over the entire space, ESMM ensures that inferences are made using samples representative of all impressions while simultaneously eliminating both sample selection bias and extreme data sparsity problems. Furthermore, ESMM employs a feature representation transfer learning strategy which helps improve model fitting even with limited data by transferring knowledge from related tasks such as item relevance prediction or user preference prediction tasks into its own task – predicting post click conversion rate – thus increasing its accuracy significantly compared to conventional methods without this technique employed..
Experimental Results
Experiments conducted on a dataset gathered from Taobao's recommender system demonstrate that ESMM outperforms competitive methods significantly in terms of precision@k metric when applied to various scenarios including cold start cases where there is no prior information available about users’ preferences or items’ relevance scores.. The authors also release a sampling version of this dataset to facilitate future research efforts aimed at improving ranking systems in industrial applications such as recommendation and advertising..
Conclusion
This paper presents an innovative approach for estimating post click conversion rate which tackles key challenges faced by conventional methods while providing valuable insights into improving ranking systems in industrial applications such as recommendation and advertising
Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling applies popular deep learning methods and achieves state-of-the-art performance. However it encounters several task-specific problems in practice, making CVR modeling challenging. For example, conventional CVR models are trained with samples of clicked impressions while utilized to make inference on the entire space with samples of all impressions. This causes a sample selection bias problem. Besides, there exists an extreme data sparsity problem, making the model fitting rather difficult. In this paper, we model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion. The proposed Entire Space Multi-task Model (ESMM) can eliminate the two problems simultaneously by i) modeling CVR directly over the entire space, ii) employing a feature representation transfer learning strategy. Experiments on dataset gathered from Taobao's recommender system demonstrate that ESMM significantly outperforms competitive methods. We also release a sampling version of this dataset to enable future research. To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
