April 12, 2024
Pinterest Engineering
Pinterest Engineering Blog

Haomiao Li | Software program Engineer, Closeup Rating & Mixing; Travis Ebesu | Software program Engineer, Closeup Rating & Mixing; Fan Jiang | Software program Engineer, Closeup Candidates; Jay Adams | Software program Engineer, Pinner Development & Alerts; Olafur Gudmundsson | Software program Engineer, Pinner Discovery; Yan Solar | Engineering Supervisor, Closeup Rating & Mixing; Huizhong Duan | Engineering Supervisor, Closeup Relevance

This Figure 1 shows the ranking model architecture from input features, to middle layer structures and the final prediction output.

At Pinterest, Closeup suggestions (aka Associated Pins) is often a feed of advisable content material (primarily Pins) that we serve on any pin closeup. Closeup suggestions generate the biggest quantity of impressions amongst all suggestion surfaces at Pinterest and are uniquely crucial for our customers’ inspiration-to-realization journey. It’s vital that we floor qualitative, related, and context-and-user-aware suggestions for individuals on Pinterest.

To realize our targets of consumer engagement and satisfaction, the Closeup relevance crew has been innovating and making use of state-of-the-art machine studying methods. Particularly, we now have designed deep neural community (DNN) fashions that deeply embed multi-task predictions for consumer outcomes. We’ve launched sequential options that seize a consumer’s most up-to-date actions, in addition to employed a personalised, context-aware mixing mannequin that mixes all predictions into remaining rating in real-time. On this weblog submit, we’ll contact on:

  • How we acquired began on multi-task prediction
  • How we additional improved multi-task prediction in our DNN structure utilizing Multi-gate Combination of Specialists (MMoE)
  • How we launched teacher-student regularization to stabilize rating mannequin predictions
  • How we integrated common consumer alerts in addition to real-time consumer sequence alerts to seize customers’ long run and brief time period curiosity
  • How we leveraged utility mixing to additional mannequin customers’ real-time, query-specific preferences

The Closeup “rating” mannequin is considerably of a misnomer as we speak. When it was first launched, it was meant to be the one mannequin that determines the rating of suggestions for Closeup suggestions. Since then, the mannequin itself, in addition to its utilization, has advanced quite a bit. Some noteworthy modifications embody using xgboost mannequin, transition to DNN, adoption of AutoML¹ , however most notably, switching from single output to multi-task prediction. On this new paradigm, the “rating” mannequin not straight determines the ultimate order for the suggestions; reasonably, it outputs the probability for various actions a consumer might take, together with closing up, repin, click on, and many others. This has led to important flexibility in optimization in addition to important enchancment within the prediction high quality. Nonetheless, we would have liked to “deepen” the multi-task modeling additional into our DNN structure by MMOE, in order that we unleashed the potential of multi-task modeling, the place every professional/job shared learnings to the utmost extent. Determine 1 is a fast view of our total DNN structure.

This Figure 1 shows the ranking model architecture from input features, to middle layer structures and the final prediction output.
Determine 1: Mannequin Structure for Closeup Feed Rating Mannequin

The Closeup rating mannequin consists of an inventory of main elements as proven in Determine 1 together with:

  • Illustration Layers: pre-processes various kinds of options (embedding desk lookup for categorical options, log transformation, and normalization for steady options, and many others.)
    – One spotlight is that we employed a transformer encoder (proven in Determine 2) to preprocess consumer sequence alerts, context options, and candidate Pin options:
    Consumer’s most up-to-date 100 engagement actions (repin, closeup, disguise, and many others.)
    Consumer’s most up-to-date 100 engaged pins’ pinSage embeddings
    Context alerts resembling question Pin embeddings and Pinner embeddings
    Candidate Pin embeddings
This Figure 2 shows how we preprocess user sequence signals, context features, and candidate Pin features via the transformer encoder.
Determine 2: Transformer Encoder for Consumer Sequence Alerts Preprocessing
  • Summarization layer: teams options which are comparable collectively (i.e. consumer annotations from completely different sources resembling search queries, board, and many others.) right into a single function by passing by a MLP, representing every function group in a decrease dimensional latent house
  • Transformer mixer: performs self-attention over teams of options
  • MMoE: combines the outcomes of unbiased “consultants” to provide predictions for every job

Under we’ll spotlight among the elements in extra element.

Multi-task Predictions

The duties that the mannequin is making an attempt to foretell are repin, closeup, clicks, and long-clicks. The mannequin discovered the likelihood by a binary entropy loss for every job, and the loss is averaged per batch throughout every coaching step. Presently the loss weight for every job is equal, however through the knowledge preparation stage, we apply numerous weight changes so that every coaching instance is correctly represented within the loss operate. The loss operate is captured under, the place b = (1, … B) from B examples within the batch, and h = (1, … H) from H duties.

Rating Regularization

Up to now, we encountered mannequin instability the place predictions throughout two fashions with the identical configuration fluctuate considerably resulting in an inconsistent consumer expertise from pointless permutations in rating order. Due to this fact we launched rating regularization⁴ (formulation is proven in Determine 3) to distill data from the instructor mannequin (the earlier manufacturing mannequin) and stabilize mannequin predictions distribution. The inference for the instructor mannequin is run throughout pupil mannequin coaching, and we add the regularization time period to whole loss and tuned the coefficient 𝜆 to manage the burden of this regularization time period.

Figure 3 shows how to distill knowledge from the teacher model and stabilize model predictions distribution for the student model.
Determine 3: Formulation of Rating Regularization

Multi-gate Combination of Specialists (MMOE)

MMoE was initially proposed on this paper² and demonstrated the power to explicitly be taught job relationships from knowledge versus the standard shared-bottom mannequin construction. The instinct is that in a share-bottom construction, mannequin parameters are tightly shared amongst duties, the place inherent battle among the many duties can hurt the predictions for a number of duties.

An MMoE module consists of a number of MLP consultants and a number of corresponding softmax gates. Every professional on this module is a MLP that makes a speciality of studying specialised job representations, and the corresponding gate will be taught the weights for every professional’s job output. Then the ultimate output is a weighted sum of the outputs from the consultants and gates, handed by a linear transformation. The location for the MMoE module is proven in Determine 4 under:

Figure 4 shows how to use multiple MLP experts in MMOE with multiple corresponding softmax gates for different tasks.
Determine 4: Combination of Specialists Structure

Some implementation particulars embody:

  • Concatenating transformer mixer output to professional output: this concept is just like ResNet, the place we not solely move the output from the transformer mixer because the enter of the consultants and gates, but additionally concatenate it to the output of the consultants. This helps to protect the total data from the transformer mixer and additional boosts mannequin efficiency.
  • Making use of 20% dropout in professional layers helps to keep away from mannequin overfitting
  • Intensive parameter tuning to search out the optimum set of hyperparameters: we carried out a grid search on three hyperparameters [num_experts, expert_hidden_sizes, tfmr_output_dim]. From the tuning, we discovered that:
    – Inside an affordable vary, the extra consultants we use, the higher the mannequin performs offline. However so as to make sure that the consultants usually are not under-utilized, we produced Determine 5 under to visualise how every professional is specialised at modeling duties.
Figure 5 shows how each expert is specialized at modeling tasks (repin, closeup, click and long click)
Determine 5: Plot Common Weights From Gates Output

— Easier professional module performs higher than wider or deeper consultants, i.e., [256, 256] provides higher efficiency than [512, 512] or [256, 256, 256]. This could possibly be as a result of we have already got a comparatively massive variety of consultants, so the consultants don’t have to be advanced.

Right here we present some offline and on-line outcomes for making use of the MMoE to rating mannequin:

  • Offline Analysis: as proven in Desk 6, for the closeup floor, we intention at enhance the HIT@3 and AUC for the 4 actions: repin (most vital one), closeup, click on and long-click as talked about in Determine 1
Hits @ 3 ROC_AUC click closeup long_click repin click closeup long_click repin MMoE +2.61% +1.58% +3.09% +1.11% +0.59% +1.31% +0.77% +0.26%
Desk 6: Offline Analysis Metrics (relative change to baseline)
  • On-line Experiment Outcomes: as proven in Desk 7, for on-line A/B experiment, we noticed that for total customers and P5 nations (US, UK, CA, FR and DE) customers, the repin quantity elevated by 4% and closeup quantity elevated by 1%, aligning with the offline analysis.
Closeup surface Total Repin Volume Total Closeup Volume All Countries P5 Countries All Countries P5 Countries MMoE +4% +3~4% +1% +1%
Desk 7: On-line Experiment Metrics

After the rating layer predictions, we make use of a mixing layer the place the order of Pin suggestions is decided. Right here, we launched one other ML mannequin, which builds upon the multi-objective optimization framework and leverages the consumer and question Pin options to make real-time choices on what to prioritize and the way a lot we need to optimize them, so as to finest serve customers’ wants in addition to to accommodate numerous enterprise necessities. Presently, the layer offers a very good steadiness between the natural content material, which optimizes for natural engagements, and procuring content material, which optimizes for procuring conversion.

The natural content material goal is at present represented as a weighted sum between hand-tuned coefficients and every job’s prediction by the rating mannequin on account of its Pareto optimality. Traditionally, the crew has been utilizing Bayesian optimization methods to tune the mixing weights by on-line experiments. However this generic method lacks robustness as we have to tune the weights every time the rating mannequin rating distribution shifts, and the suggestions loop is lengthy. Due to this fact, we launched a model-based method to be taught personalised weights, which we name Realized Utility.

Realized Utility Mannequin

We formulate studying these optimum blender parameters (coverage) into an offline supervised studying setting. For a slice of customers, we randomly fluctuate their blender parameters and log the corresponding final result. Subsequent, we outline a reward operate which assigns a price to the corresponding engagement we noticed (e.g. closeup reward = 1 and conceal reward = -2). Then we be taught a mannequin that predicts the anticipated reward for a given request. We use a mannequin that may be factored allowing entry to the discovered optimum blender parameters as proven in Determine 8. At serving time, we use solely the a part of the mannequin that predicts the optimum blender parameters as proven in Determine 9.

Figure 8 shows how to use logged user features and blender parameters for model training.
Determine 8: Coaching technique of the Realized Utility Mannequin
Figure 9 shows how to do serving inference using the online user features.
Determine 9: Serving the Realized Utility mannequin

Extra formally, Realized Utility makes an attempt to discover a set of mixing parameters w₁, … , wₙ that optimizes a given reward, R. We are able to formulate this as a binary classification job with a reward weighted cross-entropy loss denoted as R * l(g(x, r), y). Every coaching occasion is comprised of (R, x, r, y) , the place consumer, context and question degree options denoted as x; r the randomized blender parameters that led to the consumer’s engagement habits y ensuing within the reward R and our mannequin g(x, r). Our mannequin is parameterized through a multi-layer perceptron f(x) = w₁…. wₙ. To calculate the reward of the anticipated blender parameters we compute the interior product with the randomized blender parameters, ie g(x, r) = (rᵀf(x) + b), the place b is a learnable world bias and is the logistic sigmoid operate. This formulation permits us to factorize the mannequin g(.) and procure our desired blender parameters f(.).

Noise launched through the assortment of the randomized logging coverage makes it tough for the mannequin to correctly be taught a very good set of parameters. Due to this fact we place informative Gaussian priors on our blender parameters wᵢ ~N(sᵢ, σᵢ²) the place the sᵢ denotes the iᵗʰ recognized manufacturing parameter and a hyperparameter σ² to manage the variance. Performing an MAP estimation will give us an equal L2 regularizer resulting in our remaining goal

the place we simplify 𝜆ᵢ= 1/2σᵢ² and in experiments we use a worldwide 𝜆 = 2 .

On-line Experiment Outcomes

The outcomes proven under come from our on-line A/B experiment for the closeup stream floor rating and mixing stage. That is the stream expertise triggered when a consumer closes up on a natively revealed video Pin³. The important thing metrics for this floor are 10s full display view (FSV), length and time spent, and from Desk 10, we now have seen important enhancements in these metrics.

10s FSV Total Duration Reactions Engaged Stream Sessions +6.97% +2~4% +4~9% +1~2%
Desk 10: On-line Experiment Outcomes for Realized Utility

Our work of adopting and innovating upon multi-task studying with superior options and state-of-art mannequin structure within the Closeup suggestion system has successfully improved high quality of content material and led to important advantages to pinners’ engagements.

As for subsequent steps, we’re working with cross crew efforts on:

  • Adopting a richer and longer actual time consumer sequence sign
  • Enhancing GPU mannequin serving efficiency
  • Mannequin structure iterations
  • Adoption of discovered utility in different surfaces resembling Homefeed

This work represents a results of collaboration throughout a number of groups at Pinterest.

And plenty of because of the next folks that contributed to this work:

Closeup crew: Minzhen Yi , Bo Fu, Chen Chen

ATG crew: Yi-Ping Hsu, Paul Baltecsu, Pong Eksombatchai, Jiajing Xu

ML Platform crew: Nazanin Farahpour, Se Received Jang, Zhiyuan Zhang

Consumer Sequence Assist crew: Zefan Fu, Shun-ping Chiu, Jisong Liu, Yitong Zhou,Jiacheng Hong

Homefeed crew: Yaron Greif, Ruimin Zhu

Core Serving Infra crew: Kent Jiang,Zheng Liu

Search crew: Cosmin Negruseri

¹E. Wang, How we use AutoML, Multi-task studying and Multi-tower fashions for Pinterest Adverts

²J. Ma, and many others “Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts”, KDD 2018, August 19–23, 2018

³“Pinterest introduces Concept Pins globally and launches new creator discovery options”


⁴R. Li, et al “Stabilizing Neural Search Ranking Models”, WWW 2020

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