首先來介紹百度綜合資訊流推薦的業務背景、資料背景，以及基本的演算法策略。
#百度的綜合資訊流包含在手百APP中搜尋框的清單頁以及沉浸頁的形態，涵蓋了多種產品類型。從上圖可以看到，推薦內容的形式有類似抖音的沉浸態推薦，也有單列和雙列推薦，類似小紅書筆記的版面。用戶與內容的交互方式也非常多樣，可以在落地頁上進行評論、點讚、收藏，還可以進入作者頁查看相關信息並進行交互，用戶還可以提供負向的反饋等等。整個綜合資訊流的設計非常豐富多樣，能夠滿足使用者不同的需求和互動方式。
從建模的角度來看，主要面對三方面的挑戰：
#在整個產業界的推廣在搜場景上，特徵設計通常採用離散化的方式，以確保記憶和泛化兩方面的效果。特徵透過哈希方式轉換為one-hot編碼進行離散化處理。對於頭部用戶，需要進行精細刻畫，以實現準確的記憶。而對於佔比較大的稀疏長尾用戶，則需要進行良好的泛化處理。此外，在使用者的點擊和消費決策序列中，session扮演著非常重要的角色。
模型設計需要平衡頭部和長尾的資料分佈，確保準確性和泛化能力。特徵設計已經考慮到了這一點，因此模型設計也需要同時考慮到泛化和準確性。百度推薦漏斗對效能要求非常嚴格，因此需要在架構和策略上進行聯合設計，找到效能和效果的平衡點。此外，還需要平衡模型的高吞吐性和精確度。
架構的設計需要從效能和效果兩個維度綜合考慮。一個模型無法處理數千萬的資源庫，因此必須進行分層設計，核心思想是分治法。各層之間存在關聯，因此需要進行多階段的聯訓，以提升多階段漏斗之間的效率。此外，還需要採用彈性計算法，在資源幾乎不變的情況下，能夠上線複雜的模型。
上圖中右側的漢諾塔項目，在粗排這一層非常巧妙地實現了用戶與資源的分離建模。還有CTR3.0 聯合訓練，實現了多層多階段的聯訓，例如精排，是整個系統中最複雜、最精緻的模型，精度是相當高的，重排是在精排之上做list wise 的建模，精排跟重排的關係是很緊密的，我們提出的基於這兩個模型聯訓的方式，取得了非常好的線上效果。
接下來，將分別從特徵、演算法和架構三個角度進一步展開介紹。
特徵描述了使用者與系統之間的互動決策過程。
下圖中展示了使用者-資源-場景-狀態時空關係交互矩陣圖。
首先將所有訊號切分為使用者、資源、場景和狀態這四個維度，因為本質上是要建模使用者與資源之間的關係。在每個維度上，可以做各種各樣的畫像資料。
使用者維度上，最基礎的年齡、性別、興趣點畫像。在此基礎上還會有一些細微的特徵，例如相似用戶，以及用戶歷史上對不同資源類型的偏好行為等。 session 特徵，主要是長短期行為序列。業界有很多做序列的模型，在此不作贅述。但無論做何種類型的序列模型，都缺少不了特徵層面的離散 session 特徵。在百度的搜尋廣告上，從10 多年前就已經引入了這一種細粒度的序列特徵，對用戶在不同的時間窗口上，對不同資源類型的點擊行為、消費行為等等都細緻地刻畫了多組序列特徵。
資源維度上，也會有 ID 類別特徵來記錄資源本身的情況，主導的是記憶。還有明文畫像特徵來實現基礎的泛化能力。除了粗粒度的特徵以外，也會有更細緻的資源特徵，例如 Embedding 畫像特徵，是基於多模態等預訓練模型產出的，更細緻地建模離散 embedding 空間中資源之間的關係。還有統計畫像類別的特徵，描述資源各種情況下的後驗如何。以及 lookalike 特徵，透過使用者來反向表徵資源進而提升精度。
在場景維度上，有單列、沉浸式、雙列等不同的場景特徵。
使用者在不同的狀態下，對於 feed 資訊的消費也是不同的。例如刷新狀態是如何的，是從什麼樣的網路過來的，以及落地頁上的互動形態是怎麼樣的，都會影響到使用者未來的決策，所以也會從狀態維度來描述特徵。
透過使用者、資源、狀態、場景四個維度，全面刻畫使用者與系統互動的決策過程。很多時候也會做多個維度之間的組合。
#接下來介紹離散特徵設計原理。
優質的特徵通常有三個特點：區分度高、覆蓋率高、穩健性強。
除了上述三個標準，還可以做單一特徵的 AUC 判斷。例如只用某一特徵來訓練模型，看特徵跟目標的關係。也可以去掉某個特徵，看少了特徵之後的 AUC 變化。
基於上述設計原則，我們來重點討論三類重要特徵：即交叉、偏移和序列特徵。
Entire recommendation The funnel is designed in layers, with filtering and truncation at each layer. How to achieve maximum efficiency in a layered design with filter truncation? As mentioned earlier, we will do joint training of models. In addition, related designs can also be done in the dimension of feature design. There are also some problems here:
The following introduces the core algorithm the design of.
First let’s look at the recommended sorting model. It is generally believed that fine ranking is the most accurate model in the recommendation system. There is a view in the industry that rough layout is attached to fine layout and can be learned from fine layout. However, in actual practice, it has been found that rough layout cannot be directly learned from fine layout, which may cause many problems.
As you can see from the picture above, the positioning of rough sorting and fine sorting is different. Generally speaking, the rough sorting training samples are the same as the fine sorting samples, which are also display samples. Each time there are tens of thousands of candidates recalled for rough ranking, more than 99% of the resources are not displayed, and the model only uses a dozen or so resources that are finally displayed for training, which breaks the independence Under the assumption of identical distribution, the distribution of offline models varies greatly. This situation is most serious in recall, because the recall candidate sets are millions, tens of millions or even hundreds of millions, and most of the final returned results are not displayed. Rough sorting is also relatively serious. Because the candidate set is usually in the tens of thousands. The fine sorting is relatively better. After passing through the two-layer funnel of recall and rough sorting, the basic quality of resources is guaranteed. It mainly does the work of selecting the best from the best. Therefore, the problem of offline distribution inconsistency in fine ranking is not so serious, and there is no need to consider too much the problem of sample selection bias (SSB). At the same time, because the candidate set is small, heavy calculations can be done. Fine ranking focuses on feature intersection, sequence modeling, etc. .
However, the level of rough sorting cannot be directly learned from fine sorting, nor can it be directly recalculated similar to fine sorting, because the calculation amount is dozens of times that of fine sorting. Times, if you directly use the design idea of fine layout, the online machine will be completely unbearable, so rough layout requires a high degree of skill to balance performance and effect. It is a lightweight module. The focus of rough sorting iteration is different from fine sorting, and it mainly solves problems such as sample selection bias and recall queue optimization. Since rough sorting is closely related to recall, more attention is paid to the average quality of thousands of resources returned to fine sorting rather than the precise sorting relationship. Fine ranking is more closely related to rearrangement and focuses more on the AUC accuracy of a single point.
Therefore, in the design of rough ranking, it is more about the selection and generation of samples, and the design of generalization features and networks. The refined design can do complex multi-order intersection features, ultra-long sequence modeling, etc.
The previous introduction is at the macro level, let’s take a look at the micro level.
# Specific to the model training process, the current mainstream in the industry is to use ultra-large-scale discrete DNN, and the generalization problem will be more serious. Because ultra-large-scale discrete DNN, through the embedding layer, mainly performs the memory function. See the figure above. The entire embedding space is a very large matrix, usually with hundreds of billions or trillions of rows and 1,000 columns. Therefore, model training is fully distributed, with dozens or even hundreds of GPUs doing distributed training.
Theoretically, for such a large matrix, brute force calculations will not be performed directly, but operations similar to matrix decomposition will be used. Of course, this matrix decomposition is different from the standard SVD matrix decomposition. The matrix decomposition here first learns the low-dimensional representation, and reduces the amount of calculation and storage through the sharing of parameters between slots, that is, it is decomposed into two matrices. the process of learning. The first is the feature and representation matrix, which will learn the relationship between the feature and the low-dimensional embedding. This embedding is very low, and an embedding of about ten dimensions is usually selected. The other one is the embedding and neuron matrix, and the weights between each slot are shared. In this way, the storage volume is reduced and the effect is improved.
Low-dimensional embedding learning is the key to optimizing the generalization ability of offline DNN. It is equivalent to doing sparse matrix decomposition. Therefore, the key to improving the generalization ability of the entire model lies in how to make it Parameter size can be better matched with the number of samples.
Optimize from multiple aspects:
The industry usually adopts a two-stage training method to resist overfitting. The entire model consists of two layers, one is a large discrete matrix layer, and the other is a small dense parameter layer. The discrete matrix layer is very easy to overfit, so industry practice usually uses One Pass Training, that is, online learning, where all the data is passed through, and batch training is not done like in academia.
In addition, the industry usually uses timing validation set to solve the overfitting problem of sparse layers. Divide the entire training data set into many Deltas, T0, T1, T2, and T3, according to the time dimension. Each training is fixed with the discrete parameter layer trained a few hours ago, and then the next Delta data is used to finetune the dense network. That is, by fixing the sparse layer and retraining other parameters, the overfitting problem of the model can be alleviated.
This approach will also bring another problem, because the training is divided, and the discrete parameters at time T0 need to be fixed each time, and then the join is retrained at time t 1 stage, this will slow down the entire training speed and bring scalability challenges. Therefore, in recent years, single-stage training has been adopted, that is, the discrete representation layer and the dense network layer are updated simultaneously in a Delta. There is also a problem with single-stage training, because in addition to embedding features, the entire model also has many continuous-valued features. These continuous-valued features will count the display clicks of each discrete feature. Therefore, it may bring the risk of data crossing. Therefore, in actual practice, the first step will be to remove the statistical features, and the second step will be to train the dense network together with the discrete representation, using a single-stage training method. In addition, the entire embedded length is automatically scalable. Through this series of methods, model training can be accelerated by about 30%. Practice shows that the degree of overfitting of this method is very slight, and the difference between the AUC of training and testing is 1/1000 or lower.
Next, we will introduce the architecture design thoughts and experiences.
effect. Because fine ranking is not the ground truth, user behavior is. You need to learn user behavior well, not learn fine ranking. This is a very important tip.
#The relationship between fine ranking and rearrangement is It is very close. In the early years, rearrangements were directly trained using the scores of fine lineups. On the one hand, the coupling was very serious. On the other hand, the scores of fine lineups were directly used for training, which easily caused online fluctuations.
Baidu Fengchao CTR 3.0 joint training project of fine ranking and rearrangement very cleverly uses models to train at the same time to avoid the problem of scoring coupling. This project uses the hidden layer and internal scoring of the fine-ranking sub-network as characteristics of the rearrangement sub-network. Then, the fine-ranking and rearrangement sub-networks are separated and deployed in their respective modules. On the one hand, the intermediate results can be reused well without the fluctuation problem caused by scoring coupling. At the same time, the accuracy of rearrangement will be improved by a percentile. This was also one of the sub-projects that received Baidu’s highest award that year.
In addition, please note that this project is not ESSM. ESSM is CTCVR modeling and multi-objective modeling. CTR3.0 joint training mainly solves the problems of scoring coupling and rearrangement model accuracy. .
In addition, recall and rough sorting must be decoupled, because new queues are added, which may not be fair to the new queues. Therefore, a random masking method is proposed, that is, randomly masking out some features so that the coupling degree is not so strong.
Finally, let’s take a look at the online deployment process. The scale of model parameters is in the order of hundreds of billions to trillions, and there are many targets. Direct online deployment is very expensive, and we cannot only consider the effect without considering the performance. A better way is elastic calculation, similar to the idea of Sparse MOE.
Rough sorting has access to a lot of queues, with dozens or even hundreds of queues. The online value (LTV) of these queues is different. The traffic value layer calculates the value of different recall queues to online click duration. The core idea is that the greater the overall contribution of the recall queue, the more complex calculations can be enjoyed. This allows limited computing power to serve higher value traffic. Therefore, we did not use the traditional distillation method, but adopted an idea similar to Sparse MOE for elastic computing, that is, the design of strategy and architecture co-design, so that different recall queues can use the most suitable resource network for calculation.
As we all know, now Entering the era of LLM large models. Baidu's exploration of the next generation recommendation system based on LLM large language model will be carried out from three aspects.
The first aspect is to upgrade the model from basic prediction to being able to make decisions. For example, important issues such as efficient exploration of classic cold start resources, immersive sequence recommendation feedback, and the decision-making chain from search to recommendation can all be made with the help of large models.
The second aspect is from discrimination to generation. Now the entire model is discriminative. In the future, we will explore generative recommendation methods, such as automatically generating recommendation reasons, and based on long-tail data prompt for automatic data enhancement and generative retrieval model.
The third aspect is from black box to white box. In the traditional recommendation system, people often say that neural network is an alchemy and a black box. Is it possible to move towards white box? Exploration is also one of the important tasks in the future. For example, based on cause and effect, we can explore the reasons behind user behavior state transitions, make better unbiased estimates of recommendation fairness, and perform better scene adaptation in Multi Task Machine Learning scenarios.
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