search
HomeTechnology peripheralsAIApplication of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

This article explores how to implement a cross-domain recommendation model under the framework of continuous learning in the industry, and proposes a new cross-domain recommendation paradigm of continuous transfer learning, using the intermediate layer representation results of the continuously pre-trained source domain model as Based on the additional knowledge of the target domain model, a lightweight Adapter module was designed to realize the migration of cross-domain knowledge, and achieved significant business results in the ranking of recommended products.

Background

In recent years, with the application of deep models, the recommendation effect of recommendation systems in the industry has been significantly improved. With the continuous optimization of models, only It becomes more difficult to optimize model structure and features relying on in-scenario data. On large-scale e-commerce platforms like Taobao, in order to meet the diverse needs of different users, there are a series of recommendation scenarios of different sizes, such as information flow recommendation (you may like it on the home page), good products, post-purchase recommendations, and gathering These scenarios share the Taobao product system, but there are significant differences in specific product selection pools, core users, and business goals, and the scale of different scenarios varies greatly. Our scenario of "Good Goods" is a shopping guide scenario for Taobao's selected products. Compared with information flow recommendation, main search and other scenarios, the scale is relatively small. Therefore, how to use transfer learning, cross-domain recommendation and other methods to improve the model effect has always been It is one of the key points in optimizing the good goods sorting model. Although products and users in Taobao’s different business scenarios overlap, due to the significant differences in scenarios, the ranking model for large scenarios such as information flow recommendations does not work well when directly applied to scenarios where good products are available. Therefore, the team has made considerable attempts in the direction of cross-domain recommendation, including using a series of existing methods such as pre-training and fine-tuning, multi-scenario joint training, and global learning. These methods are either not effective enough or have quite a few problems in actual online applications. The continuous transfer learning project proposes a simple and effective new cross-domain recommendation method for a series of problems in the application of these methods in business. This method

uses the intermediate layer representation results of the continuously pre-trained source domain model as additional knowledge of the target domain model

, and has achieved significant business results in the ranking of good product recommendations on Taobao.

The detailed version of this article Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao has been published on ArXiv https://arxiv.org/abs/2208.05728.

Method

▐ Existing work and its shortcomings

Analysis of existing cross-domain work in academia and industry Cross-Domain Recommendation (CDR) related work can be mainly divided into two categories: Joint Learning and Pre-training & Fine-tuning. Among them, the joint training method simultaneously optimizes the source domain (Source Domain) and target domain (Target Domain) models. However, this type of method requires the introduction of source domain data in training, and source domain samples are usually large in size, thus consuming huge computing and storage resources. Many smaller businesses cannot afford such a large resource overhead. On the other hand, this type of method needs to optimize multiple scene goals at the same time, and differences between scenes may also bring negative effects of goal conflicts. Therefore, pre-training-fine-tuning methods have wider applications in many scenes in the industry.

An important feature of the industrial recommendation system is that model training follows the Continual Learning (Continual Learning) paradigm, that is, the model needs to use the latest samples and utilize offline incremental updates (Incremental Learning) or Online Learning and other methods to learn the latest data distribution. For the cross-domain recommendation task studied in this article, the models in the source domain and the target domain both follow the continuous learning training method. We thus propose a new problem that will be widely used in academic and industrial applications: Continual Transfer Learning, defined as moving from one domain that changes over time to another that also changes over time. Domain knowledge transfer. We believe that the application of existing cross-domain recommendation and transfer learning methods in industrial recommendation systems, search engines, computational advertising, etc. should follow the continuous transfer learning paradigm, that is, the transfer process should be continuous and multiple times. The reason is that the data distribution changes rapidly, and only continuous migration can ensure a stable migration effect. Combined with the characteristics of this industrial recommendation system, we can find problems in the practical application of pre-training and fine-tuning. Due to the scene differences between the source domain and the target domain, it is usually necessary to use a large number of samples to obtain a better result by fine-tuning the source domain model. In order to achieve continuous transfer learning, we need to use the latest source domain model to re-fine-tune it every once in a while, resulting in a very huge training cost. This training method is also difficult to go online. In addition, using these large number of samples for fine-tuning may also cause the source domain model to forget the retained useful knowledge, avoiding the catastrophic forgetting problem in the model; using the source domain model parameters to replace the original parameters that have been learned in the target domain can also Useful knowledge gained historically from the original model is discarded. Therefore, we need to design a more efficient continuous transfer learning model suitable for industrial recommendation scenarios.

This article proposes a simple and effective model CTNet (Continual Transfer Network, continuous migration network) to solve the above problems. Different from traditional pre-training-fine-tuning methods, the core idea of ​​CTNet is that cannot forget and discard all the knowledge acquired by the model in history, and retains all parameters of the original source domain model and target domain model . These parameters store knowledge gained through very long historical data learning (for example, Taobao's fine ranking model has been continuously incrementally trained for more than two years). CTNet adopts a simple twin-tower structure and uses a lightweight Adapter layer to map the intermediate layer representation results of the continuously pre-trained source domain model as additional knowledge of the target domain model. Unlike pre-training-fine-tuning methods that require backtracking data to achieve continuous transfer learning, CTNet only requires incremental data to be updated, thereby achieving efficient continuous transfer learning.

method

No need to use a large number of source domain samples

Not affected by source domain scenario targets

Only incremental data is needed to achieve Continuous transfer learning

Joint training

No

No

Yes

Pre-training-Fine-tuning

Yes

Yes

No

The CTNet proposed in this article

is

is

yes

Table 1: Comparison between CTNet and existing cross-domain recommendation models

##▐ Problem Definition

This article explores the new issue of continuous transfer learning:

Given the source domain and target domain that continue to change over time, continuous transfer learning (Continual Transfer Learning) hopes to be able to Use historical or currently acquired source domain and target domain knowledge to improve prediction accuracy in the future target domain.

We apply the problem of continuous transfer learning to Taobao's cross-domain recommendation task. This task has the following characteristics:

  1. # The scale of different recommendation scenarios varies greatly, and the knowledge of the model trained in the larger source domain can be used to improve the recommendation effect of the target domain.
  2. Users and products in different scenarios share the same big pot. However, there are obvious field differences in different scenarios due to different display effects of selected product pools, core users, graphics and texts, etc.
  3. All recommended scenario models are continuously incrementally trained based on the latest data.

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

Figure 1: Schematic diagram of model deployment

Above picture Shows the deployment of our method online. Before the Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system moment, the source domain model and the target domain model were trained individually and continuously incrementally using only the supervision data of the respective scenes. Starting from the Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system moment, we deployed the cross-domain recommendation model CTNet on the target domain. This model will continue to increment on the target domain data without forgetting the knowledge acquired in history. training while continuously transferring knowledge from the latest source domain model.

▐ Continuous migration network model (CTNet)

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

Figure 2: Continuous migration network CTNet

As shown in Figure 2, the Continuous Transfer Network (CTNet) model we proposed embeds all the source domain models in the original fine-ranking model of the target domain. The features and their network parameters form a two-tower structure, in which the left tower of CTNet is the Source Tower and the right tower is the Target Tower. Different from the common methods that only use the final scoring score of the source domain model or only use some shallow representations (such as Embedding), we use a lightweight Adapter network to combine all the intermediate hidden layers of the source domain modelMLP (Especially the high-order feature interaction information of user and item contained deeply in the source domain MLP), the representation result Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system is mapped to the target recommendation domain, and Add the result to the corresponding layer Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system# of Target Tower (the formula below represents the situation of Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system).

The key to improving the effect of CTNet is to utilize the migration of deep representation information in MLP. Drawing on the idea of ​​Gated Linear Units (GLU), the Adapter network uses a gated linear layer, which can effectively implement adaptive feature selection of source domain features. Useful knowledge in the model will be migrated, and information that is inconsistent with the scene characteristics will be transferred. can be filtered out. Since the source domain model continues to use the latest source domain supervision data for continuous pre-training, during our training process, Source Tower will also continue to load the latest updated source domain model parameters and remain fixed during the backpropagation process. , ensuring the efficient progress of continuous transfer learning. Therefore, the CTNet model is very suitable for the continuous learning paradigm, allowing the target domain model to continuously learn the latest knowledge provided by the source domain model to adapt to the latest data distribution changes. At the same time, because the model is only trained on the target domain data, it is ensured that the model is not affected by the source domain training objectives, and does not require source domain data training at all, avoiding a large amount of storage and computing overhead. In addition, such a network structure adopts an additive design method, so that the dimensions of the MLP layer of the original model do not need to be changed during the migration process. Target Tower is completely initialized by the original target domain online model, avoiding random re-initialization of the MLP layer. It can be It ensures that the effect of the original model is not damaged to the greatest extent, and only requires less incremental data to obtain good results, realizing hot start of the model.

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

We define the source domain model as Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system, the original single-domain recommended target domain model is Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system, the newly deployed target domain cross-domain recommendation model is Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system, Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system is the cross-domain recommendation model deployment online time, the The model is continuously incrementally updated to time Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system. The parameters of Adapter, Source Tower and Target Tower are Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system, Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system and Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system respectively. The process of CTNet training is as follows:

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

##Figure 3: CTNet training


Experiment

▐ Offline effect

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

Table 2: Offline experimental results

As shown in the table above, we have the corresponding production data sets in the two sub-scenarios (Domain B and C in the table) with good goods business A series of offline experiments were conducted on the website, in which the source domain (Domain A in the table) is the homepage information flow recommendation scenario. It can be seen that the direct use of information flow recommendations (you may like it on the homepage) ranking model scoring results (Source Domain Model in the table) is not effective in the business of good goods. Compared with the online full volume model, the absolute value is GAUC-5.88. % and GAUC-9.06%, proving the differences between scenarios.

We also compared a series of traditional cross-domain recommendation Baseline methods, including common pre-training-fine-tuning methods and joint training methods (such as MLP, PLE, MiNet, DDTCDR, DASL, etc.), and proposed CTNet significantly outperforms existing methods on both data sets. Compared with the full online main model, CTNet achieved significant improvements in GAUC of 1.0% and 3.6% on the two data sets respectively. We further analyzed the advantages of continuous transfer compared to single transfer through experiments. Under the framework of CTNet, the effect improvement brought by a single transfer will attenuate with the incremental update of the model, while continuous transfer learning can ensure the stable improvement of the model effect.

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

Figure 4: Advantages of continuous transfer learning compared to single transfer

The following table shows the effect of traditional pre-training-fine-tuning. We use the complete source domain model to train on the target domain data. Due to the differences between fields, a very large number of samples (such as 120-day samples) are needed to adjust the model's effect to a level comparable to the full online Base model. In order to achieve continuous transfer learning, we need to re-adjust using the latest source domain model at regular intervals. The huge cost of each adjustment also makes this method unsuitable for continuous transfer learning. In addition, this method does not surpass the base model without migration in terms of effect. The main reason is that the use of massive target domain sample training also causes the model to forget the original source domain knowledge, and the final model effect obtained by training is similar to a The effect of training only on target domain data. Under the pre-training-fine-tuning paradigm, loading only some Embedding parameters is better than reusing all parameters (as shown in Table 2).

Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system

Table 3: Effect of training on the target domain using pre-trained source domain model

▐ Online effect

CTNet will be completed by the end of 2021 at the earliest, and will be fully launched in February 2022 for the recommendation business of good products. Compared with the previous generation full model, significant improvements in business indicators have been achieved in two recommendation scenarios: Scenario B: CTR 2.5%, additional purchases 6.7%, number of transactions 3.4%, GMV 7.7%C scenario: CTR 12.3%, length of stay 8.8%, additional purchase 10.9%, number of transactions 30.9%, GMV 31.9%

##CTNet adopts a parallel network structure. To save computing resources, we share some parameters and results of the Attention layer, so that the same parts of the Attention layer in Source Tower and Target Tower only need to be calculated once. Compared with the Base model, CTNet's online response time (RT) is basically the same.

Summary and Outlook

This article explores how to implement a cross-domain recommendation model under the framework of continuous learning in the industry, and proposes a new cross-domain recommendation model called continuous transfer learning. The domain recommendation paradigm uses the intermediate layer representation results of the continuously pre-trained source domain model as additional knowledge of the target domain model. A lightweight Adapter module is designed to realize the transfer of cross-domain knowledge and achieves good product recommendation ranking. achieved significant business results. Although this method is implemented for the business characteristics of good goods, it is also a relatively general modeling method. The related modeling methods and ideas can be applied to the optimization of many other similar business scenarios. Since the existing continuous pre-trained source domain model of CTNet only uses information flow recommendation scenarios, in the future we will consider upgrading the continuously pre-trained source domain model to a full-domain learning pre-trained model including recommendation, search, private domain and other more scenarios. Train the model.

Team introduction

We are the Taobao technology-content algorithm-good goods algorithm team. Good products are recommended by Taobao based on word-of-mouth, and are a shopping guide designed to help consumers discover good products. The team is responsible for optimizing the full-link algorithm for product recommendation and short video content recommendation business to improve advantageous product mining capabilities and channel shopping guide capabilities. The current main technical directions are continuous transfer learning cross-domain recommendation, unbiased learning, recommendation system full-link modeling, sequence modeling, etc. While creating business value, we have also published several papers at international conferences such as SIGIR. The main results include PDN, UMI, CDAN, etc.

The above is the detailed content of Application of continuous transfer learning cross-domain recommendation ranking model in Taobao recommendation system. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:51CTO.COM. If there is any infringement, please contact admin@php.cn delete
The AI Skills Gap Is Slowing Down Supply ChainsThe AI Skills Gap Is Slowing Down Supply ChainsApr 26, 2025 am 11:13 AM

The term "AI-ready workforce" is frequently used, but what does it truly mean in the supply chain industry? According to Abe Eshkenazi, CEO of the Association for Supply Chain Management (ASCM), it signifies professionals capable of critic

How One Company Is Quietly Working To Transform AI ForeverHow One Company Is Quietly Working To Transform AI ForeverApr 26, 2025 am 11:12 AM

The decentralized AI revolution is quietly gaining momentum. This Friday in Austin, Texas, the Bittensor Endgame Summit marks a pivotal moment, transitioning decentralized AI (DeAI) from theory to practical application. Unlike the glitzy commercial

Nvidia Releases NeMo Microservices To Streamline AI Agent DevelopmentNvidia Releases NeMo Microservices To Streamline AI Agent DevelopmentApr 26, 2025 am 11:11 AM

Enterprise AI faces data integration challenges The application of enterprise AI faces a major challenge: building systems that can maintain accuracy and practicality by continuously learning business data. NeMo microservices solve this problem by creating what Nvidia describes as "data flywheel", allowing AI systems to remain relevant through continuous exposure to enterprise information and user interaction. This newly launched toolkit contains five key microservices: NeMo Customizer handles fine-tuning of large language models with higher training throughput. NeMo Evaluator provides simplified evaluation of AI models for custom benchmarks. NeMo Guardrails implements security controls to maintain compliance and appropriateness

AI Paints A New Picture For The Future Of Art And DesignAI Paints A New Picture For The Future Of Art And DesignApr 26, 2025 am 11:10 AM

AI: The Future of Art and Design Artificial intelligence (AI) is changing the field of art and design in unprecedented ways, and its impact is no longer limited to amateurs, but more profoundly affecting professionals. Artwork and design schemes generated by AI are rapidly replacing traditional material images and designers in many transactional design activities such as advertising, social media image generation and web design. However, professional artists and designers also find the practical value of AI. They use AI as an auxiliary tool to explore new aesthetic possibilities, blend different styles, and create novel visual effects. AI helps artists and designers automate repetitive tasks, propose different design elements and provide creative input. AI supports style transfer, which is to apply a style of image

How Zoom Is Revolutionizing Work With Agentic AI: From Meetings To MilestonesHow Zoom Is Revolutionizing Work With Agentic AI: From Meetings To MilestonesApr 26, 2025 am 11:09 AM

Zoom, initially known for its video conferencing platform, is leading a workplace revolution with its innovative use of agentic AI. A recent conversation with Zoom's CTO, XD Huang, revealed the company's ambitious vision. Defining Agentic AI Huang d

The Existential Threat To UniversitiesThe Existential Threat To UniversitiesApr 26, 2025 am 11:08 AM

Will AI revolutionize education? This question is prompting serious reflection among educators and stakeholders. The integration of AI into education presents both opportunities and challenges. As Matthew Lynch of The Tech Edvocate notes, universit

The Prototype: American Scientists Are Looking For Jobs AbroadThe Prototype: American Scientists Are Looking For Jobs AbroadApr 26, 2025 am 11:07 AM

The development of scientific research and technology in the United States may face challenges, perhaps due to budget cuts. According to Nature, the number of American scientists applying for overseas jobs increased by 32% from January to March 2025 compared with the same period in 2024. A previous poll showed that 75% of the researchers surveyed were considering searching for jobs in Europe and Canada. Hundreds of NIH and NSF grants have been terminated in the past few months, with NIH’s new grants down by about $2.3 billion this year, a drop of nearly one-third. The leaked budget proposal shows that the Trump administration is considering sharply cutting budgets for scientific institutions, with a possible reduction of up to 50%. The turmoil in the field of basic research has also affected one of the major advantages of the United States: attracting overseas talents. 35

All About Open AI's Latest GPT 4.1 Family - Analytics VidhyaAll About Open AI's Latest GPT 4.1 Family - Analytics VidhyaApr 26, 2025 am 10:19 AM

OpenAI unveils the powerful GPT-4.1 series: a family of three advanced language models designed for real-world applications. This significant leap forward offers faster response times, enhanced comprehension, and drastically reduced costs compared t

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

MinGW - Minimalist GNU for Windows

MinGW - Minimalist GNU for Windows

This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool

SublimeText3 Linux new version

SublimeText3 Linux new version

SublimeText3 Linux latest version

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

Dreamweaver Mac version

Dreamweaver Mac version

Visual web development tools