Deep Multi-task Studying and Actual-time Personalization for Closeup Suggestions | by Pinterest Engineering | Pinterest Engineering Weblog | Jun, 2023

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 & Indicators; 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 really useful content material (primarily Pins) that we serve on any pin closeup. Closeup suggestions generate the biggest quantity of impressions amongst all advice 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 folks on Pinterest.

To attain our targets of consumer engagement and satisfaction, the Closeup relevance crew has been innovating and making use of state-of-the-art machine studying strategies. Particularly, we have now 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 ultimate rating in real-time. On this weblog publish, 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 Consultants (MMoE)
  • How we launched teacher-student regularization to stabilize rating mannequin predictions
  • How we included basic consumer indicators in addition to real-time consumer sequence indicators to seize customers’ long run and quick 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 lots. Some noteworthy modifications embrace the usage of 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 instantly determines the ultimate order for the suggestions; somewhat, it outputs the chance for various actions a consumer could take, together with closing up, repin, click on, and so forth. 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 skilled/activity 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 a listing of main parts 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 so forth.)
    – One spotlight is that we employed a transformer encoder (proven in Determine 2) to preprocess consumer sequence indicators, context options, and candidate Pin options:
    Person’s most up-to-date 100 engagement actions (repin, closeup, conceal, and so forth.)
    Person’s most up-to-date 100 engaged pins’ pinSage embeddings
    Context indicators equivalent to 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 Person Sequence Indicators Preprocessing
  • Summarization layer: teams options which are comparable collectively (i.e. consumer annotations from totally different sources equivalent to search queries, board, and so forth.) right into a single function by passing by a MLP, representing every function group in a decrease dimensional latent area
  • Transformer mixer: performs self-attention over teams of options
  • MMoE: combines the outcomes of impartial “specialists” to supply predictions for every activity

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

Multi-task Predictions

The duties that the mannequin is attempting to foretell are repin, closeup, clicks, and long-clicks. The mannequin discovered the chance by a binary entropy loss for every activity, and the loss is averaged per batch throughout every coaching step. At present the loss weight for every activity is equal, however through the knowledge preparation stage, we apply varied weight changes so that every coaching instance is correctly represented within the loss perform. The loss perform is captured beneath, the place b = (1, … B) from B examples within the batch, and h = (1, … H) from H duties.

Rating Regularization

Prior to now, we encountered mannequin instability the place predictions throughout two fashions with the identical configuration differ considerably resulting in an inconsistent consumer expertise from pointless permutations in rating order. Subsequently we launched rating regularization⁴ (formulation is proven in Determine 3) to distill information from the trainer mannequin (the earlier manufacturing mannequin) and stabilize mannequin predictions distribution. The inference for the trainer mannequin is run throughout pupil mannequin coaching, and we add the regularization time period to whole loss and tuned the coefficient 𝜆 to regulate the load 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 Consultants (MMOE)

MMoE was initially proposed on this paper² and demonstrated the flexibility to explicitly study activity relationships from knowledge versus the normal 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 specialists and a number of corresponding softmax gates. Every skilled on this module is a MLP that focuses on studying specialised activity representations, and the corresponding gate will study the weights for every skilled’s activity output. Then the ultimate output is a weighted sum of the outputs from the specialists and gates, handed by a linear transformation. The position for the MMoE module is proven in Determine 4 beneath:

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

Some implementation particulars embrace:

  • Concatenating transformer mixer output to skilled output: this concept is just like ResNet, the place we not solely go the output from the transformer mixer because the enter of the specialists and gates, but additionally concatenate it to the output of the specialists. This helps to protect the total data from the transformer mixer and additional boosts mannequin efficiency.
  • Making use of 20% dropout in skilled layers helps to keep away from mannequin overfitting
  • In depth 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 inexpensive vary, the extra specialists we use, the higher the mannequin performs offline. However with the intention to be certain that the specialists are usually not under-utilized, we produced Determine 5 beneath to visualise how every skilled 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

— Less complicated skilled module performs higher than wider or deeper specialists, i.e., [256, 256] offers higher efficiency than [512, 512] or [256, 256, 256]. This might be as a result of we have already got a comparatively massive variety of specialists, so the specialists don’t should 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 goal 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 set. 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 selections on what to prioritize and the way a lot we wish to optimize them, with the intention to greatest serve customers’ wants in addition to to accommodate varied enterprise necessities. At present, the layer gives a great stability 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 the moment represented as a weighted sum between hand-tuned coefficients and every activity’s prediction by the rating mannequin because of its Pareto optimality. Traditionally, the crew has been utilizing Bayesian optimization strategies 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. Subsequently, we launched a model-based method to study customized weights, which we name Discovered Utility.

Discovered Utility Mannequin

We formulate studying these optimum blender parameters (coverage) into an offline supervised studying setting. For a slice of customers, we randomly differ their blender parameters and log the corresponding end result. Subsequent, we outline a reward perform which assigns a worth to the corresponding engagement we noticed (e.g. closeup reward = 1 and conceal reward = -2). Then we study 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 Discovered Utility Mannequin
Figure 9 shows how to do serving inference using the online user features.
Determine 9: Serving the Discovered Utility mannequin

Extra formally, Discovered Utility makes an attempt to discover a set of mixing parameters w₁, … , wₙ that optimizes a given reward, R. We will formulate this as a binary classification activity 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 by way of a multi-layer perceptron f(x) = w₁…. wₙ. To calculate the reward of the expected 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 perform. This formulation permits us to factorize the mannequin g(.) and acquire our desired blender parameters f(.).

Noise launched through the assortment of the randomized logging coverage makes it tough for the mannequin to correctly study a great set of parameters. Subsequently we place informative Gaussian priors on our blender parameters wᵢ ~N(sᵢ, σᵢ²) the place the sᵢ denotes the iᵗʰ identified manufacturing parameter and a hyperparameter σ² to regulate the variance. Performing an MAP estimation will give us an equal L2 regularizer resulting in our ultimate goal

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

On-line Experiment Outcomes

The outcomes proven beneath 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 have now 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 Discovered Utility

Our work of adopting and innovating upon multi-task studying with superior options and state-of-art mannequin structure within the Closeup advice 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
  • Bettering GPU mannequin serving efficiency
  • Mannequin structure iterations
  • Adoption of discovered utility in different surfaces equivalent to Homefeed

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

And lots of because of the next those 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

Person Sequence Help 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 Advertisements

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

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

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

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