Wang Shusen Recommender Systems Study Notes — Improving Metrics

Methods for Improving Metrics

Methods for Improving Metrics

Evaluation Metrics for Recommender Systems

• Daily Active Users ($DAU$) and retention are the most critical metrics.

• The industry currently uses $LT7$ and $LT30$ most commonly to measure retention.

  • If a user logs into the app today ($t_0$) and logs in on 4 of the next 7 days ($t_0 \sim t_6$), their $LT7$ for today ($t_0$) equals 4.
  • Clearly $1 \leq LT7 \leq 7$ and $1 \leq LT30 \leq 30$.
  • Growth in $LT$ typically indicates improved user experience. (Unless $LT$ grows while $DAU$ falls.)
  • If the app bans low-activity users, $DAU$ drops and $LT$ grows.

• Other core metrics: user session duration, total reads (i.e., total clicks), total impressions. These are less important than $DAU$ and retention.

  • When session duration grows, $LT$ typically grows.
  • When session duration grows, reads and impressions may fall.

• Non-core metrics: click-through rate, interaction rate, etc.

• For UGC platforms, publishing volume and publishing penetration rate are also core metrics.

What Methods Are Available to Improve Metrics?

1. Improve retrieval models and add new retrieval channels.
2. Improve pre-ranking and full-ranking models.
3. Improve diversity in retrieval, pre-ranking, and full ranking.
4. Apply special treatment to new users, low-activity users, and other special groups.
5. Leverage the three interaction behaviors: follows, shares, and comments.

Improving Metrics: Retrieval

Retrieval Models & Retrieval Channels

• Recommender systems have dozens of retrieval channels with a fixed total retrieval quota. A larger quota leads to better metrics but higher pre-ranking compute cost.
• Two-tower models ($two\text{-}tower$) and item-to-item ($I2I$) are the two most important retrieval model classes, occupying the majority of the retrieval quota.
• Many niche models occupy very little quota. Adding certain retrieval models can improve core metrics while keeping total retrieval volume fixed.
• Multiple content pools exist: e.g., 30-day items, 1-day items, 6-hour items, new user high-quality pool, user-segment-specific pools.
• The same model can be used with multiple content pools, yielding multiple retrieval channels.

Improving Two-Tower Models

Direction 1: Optimize positive and negative samples.

• Simple positive samples: (user, item) pairs with clicks.
• Simple negative samples: randomly combined (user, item) pairs.
• Hard negative samples: (user, item) pairs ranked low by the ranking models.

Direction 2: Improve neural network architecture.

• Baseline: User tower and item tower are each fully connected networks, each outputting a single vector as user/item representation.
• Improvement: Replace fully connected networks in user and item towers with $DCN$.
• Improvement: Use user behavior sequences ($last\text{-}n$) in the user tower.
• Improvement: Replace single-vector model with multi-vector model. (The standard two-tower model is also called a single-vector model.)

Direction 3: Improve model training methods.

• Baseline: Binary classification — teach the model to distinguish positive from negative samples.
• Improvement: Combine binary classification with in-batch negative sampling. (For in-batch negative sampling, apply debiasing.)
• Improvement: Apply self-supervised learning to improve embeddings for long-tail items.

Item-to-Item (I2I)

• $I2I$ is a broad class of models that retrieve based on similar items.

• The most common usage is $U2I2I$ ($user \rightarrow item \rightarrow item$).

  • User $u$ likes item $i_1$ (an item the user has interacted with historically).
  • Find $i_1$'s similar items $i_2$ (i.e., $I2I$).
  • Recommend $i_2$ to $u$.

• How to compute item similarity?

• Method 1: ItemCF and its variants.

  • Some users like both items $i_1$ and $i_2$; then $i_1$ and $i_2$ are considered similar.
  • $ItemCF$, $Online\ ItemCF$, $Swing$, $Online\ Swing$ all share this underlying idea.
  • Use all 4 $I2I$ models simultaneously online, each with a specific quota.

• Method 2: Compute vector similarity based on item vector representations. (Both two-tower models and graph neural networks can compute item vector representations.)

Niche Retrieval Models

I2I-like Models

• U2U2I ($user \rightarrow user \rightarrow item$): Given that user $u_1$ is similar to $u_2$ and $u_2$ likes item $i$, recommend item $i$ to $u_1$.
• U2A2I ($user \rightarrow author \rightarrow item$): Given that user $u$ likes author $a$ and $a$ published item $i$, recommend item $i$ to $u$.
• U2A2A2I ($user \rightarrow author \rightarrow author \rightarrow item$): Given that user $u$ likes author $a_1$, $a_1$ is similar to $a_2$, and $a_2$ published item $i$, recommend item $i$ to $u$.

Summary: Improving Retrieval Models

• Two-tower models: Optimize positive/negative samples, improve neural network architecture, improve training methods.
• I2I models: Use $ItemCF$ and its variants simultaneously; compute item similarity using item vector representations.
• Add niche retrieval models, such as $PDN$, $Deep\ Retrieval$, $SINE$, $M2GRL$, etc.
• Adjust quotas across retrieval channels while keeping total retrieval volume fixed. (Different quotas can be set for different user segments.)

Improving Metrics: Ranking Models

Ranking Models

1. Improving the full-ranking model
2. Improving the pre-ranking model
3. User behavior sequence modeling
4. Online learning
5. Aged model ("old soup model")

Improving the Full-Ranking Model

Full-Ranking Model: Backbone

• The backbone takes discrete and continuous features as input and outputs a vector, which serves as input to multi-objective estimation.
• Improvement 1: Widen and deepen the backbone for more compute and better predictions.
• Improvement 2: Automated feature crossing, e.g., $bilinear$ [1] and $LHUC$ [2].
• Improvement 3: Feature engineering, e.g., adding statistical features and multimodal content features.

Full-Ranking Model: Multi-Objective Estimation

• Based on the backbone output vector, simultaneously estimate multiple objectives such as click-through rate.

• Improvement 1: Add new estimation targets and incorporate their estimated results into the fusion formula.

  • Standard targets include CTR, like rate, favorite rate, share rate, comment rate, follow rate, completion rate...
  • Discover additional targets, e.g., entering the comment section, liking comments written by others...
  • Add new estimation targets to the fusion formula.

• Improvement 2: Structures like $MMoE$ and $PLE$ may help but often do not.

• Improvement 3: Correcting $position\ bias$ may help, or may not.

Improving the Pre-Ranking Model

Pre-Ranking Model

• Pre-ranking scores 10× more items than full ranking; the pre-ranking model must be fast.
• Simple model: Multi-vector two-tower model, simultaneously estimating multiple targets like CTR.
• Complex model: Three-tower model performs well but is harder to implement in engineering.

Pre-ranking / Full-ranking Consistency Modeling

• Distill full-ranking to train pre-ranking, making pre-ranking more consistent with full ranking.

• Method 1: Pointwise distillation.

  • Let $y$ be the user's true behavior; let $p$ be the full-ranking model's prediction.
  • Use $\frac{y + p}{2}$ as the pre-ranking model's training target.
  • Example:
    • For CTR target: user clicked ($y=1$), full-ranking predicts $p=0.6$.
    • Use $\frac{y + p}{2} = 0.8$ as the pre-ranking's CTR target.

• Method 2: Pairwise or listwise distillation.

  • Given $k$ candidate items, rank them according to full-ranking predictions.
  • Apply learning to rank ($LTR$), training pre-ranking to fit item order (not values).
  • Example:
    • For items $i$ and $j$, full-ranking predicts CTR $p_i > p_j$.
    • $LTR$ encourages pre-ranking predictions to satisfy $q_i > q_j$; otherwise a penalty is applied.
    • $LTR$ typically uses pairwise logistic loss.

• Advantage: Pre-ranking / full-ranking consistency modeling can improve core metrics.

• Disadvantage: If full ranking has a bug and its predictions $p$ are biased, this pollutes pre-ranking training data.

User Behavior Sequence Modeling

• The simplest method is to average item vectors as a user feature.
• $DIN$ uses an attention mechanism to compute a weighted average of item vectors.
• The industry is currently evolving along the $SIM$ direction: first filter items by category and other attributes, then use $DIN$ to compute a weighted average of the filtered item vectors.

User Behavior Sequence Modeling

• Improvement 1: Increase sequence length for more accurate predictions, at the cost of higher compute and longer inference time.

• Improvement 2: Filtering methods, e.g., by category or by item vector clustering.

  • Offline: use a multimodal neural network to extract item content features and represent items as vectors.
  • Offline: cluster item vectors into 1000 classes; each item has a cluster ID.
  • Online during ranking: the user's behavior sequence has $n = 1,000,000$ items. A candidate item has cluster ID 70; filter the $n$ items to keep only those with cluster ID 70. Only a few thousand of the $n$ items are retained.
  • Multiple filtering methods are used simultaneously; take the union of filtered results.

• Improvement 3: Use features beyond IDs for items in the user behavior sequence.

• Summary: Evolve along the $SIM$ direction — keep the raw sequence as long as possible, then apply filtering to reduce sequence length, then feed filtered results into $DIN$.

Online Learning

Resource Consumption of Online Learning

• Requires both full batch updates at midnight and continuous incremental updates throughout the day.

• Suppose online learning requires 10,000 $CPU\ cores$ to incrementally update a single full-ranking model. How much additional compute does the entire recommender system need for online learning?

• For A/B testing, multiple different models run simultaneously online.

• If there are $m$ models online, $m$ sets of online learning machines are needed.

• Of $m$ models online: 1 is $holdout$, 1 is fully rolled out, and $m-2$ are new models under test.

• Each set of online learning machines has high cost, so $m$ is small, constraining model development iteration efficiency.

• Online learning brings large metric improvements but constrains model development iteration efficiency.

Aged Model ("Old Soup Model")

• Train the model for 1 $epoch$ per day using newly generated data.
• Over time, the aged model becomes extremely well-trained and difficult to surpass.
• Improving the model architecture and retraining makes it very hard to catch up with the aged model...
• Problem 1: How to quickly determine if a new model architecture is better than the aged model? (No need to actually catch up with the online aged model — just determine which architecture is better.)
  • For both new and old model architectures, randomly initialize fully connected layers.
  • Embedding layers can be randomly initialized or reuse parameters from the trained aged model.
  • Train both new and old models on $n$ days of data. (Train 1 $epoch$ from oldest to newest.)
  • If the new model is significantly better, it is likely superior.
  • Only compare which architecture is better, not actually catch up with the aged model.
• Problem 2: How to more quickly catch up with and surpass the online aged model? (Train the new model on only a few dozen days of data, yet match a model trained for hundreds of days.)
  • Having already concluded the new model is likely better, train it on a few dozen days of data to catch up with the aged model quickly.
  • Method 1: Reuse as many embedding layers from the aged model as possible; avoid random initialization of embedding layers. (Embedding layers encode "memory" of users and items and learn more slowly than fully connected layers.)
  • Method 2: Use the aged model as $teacher$ to distill the new model. (True user behavior is $y$; aged model's prediction is $p$; use $\frac{y + p}{2}$ as the training target for the new model.)

Summary: Improving Ranking Models

• Full-ranking model: Improve model backbone (wider/deeper, feature crossing, feature engineering); improve multi-objective estimation (new targets, $MMoE$, $position\ bias$).
• Pre-ranking model: Three-tower model (replacing multi-vector two-tower); pre-ranking/full-ranking consistency modeling.
• User behavior sequence modeling: Evolve along the $SIM$ direction — extend sequence length, improve item filtering methods.
• Online learning: Large metric improvements, but reduces model iteration efficiency.
• Aged model constrains model iteration efficiency; requires special techniques.

Improving Metrics: Diversity

Ranking Diversity

Full-Ranking Diversity

• Full-ranking stage: Rank item $i$ combining interest score and diversity score.

  • $s_i$: Interest score, i.e., fused score from CTR and other estimated targets.
  • $d_i$: Diversity score, i.e., difference between item $i$ and already selected items.
  • Rank items using $s_i + d_i$.

• Commonly use MMR, DPP, and similar methods to compute diversity scores; full ranking uses a sliding window, pre-ranking does not.

  • Full ranking determines final exposure; adjacent exposed items should have low similarity. So computing full-ranking diversity uses a sliding window.
  • Pre-ranking should consider overall diversity, not just diversity within a sliding window.

• Beyond diversity scores, full ranking uses diversification strategies to increase diversity.

  • Category: After selecting item $i$, the next 5 positions cannot share $i$'s second-level category.
  • Multimodal: Precompute multimodal content vector representations for items and cluster the full catalog into 1000 groups; during full ranking, after selecting item $i$, the next 10 positions cannot belong to the same cluster as $i$.

Pre-Ranking Diversity

• Pre-ranking scores 5000 items and selects 500 to send to full ranking.
• Improving diversity in both pre-ranking and full ranking can improve core recommender system metrics.
• Rank 5000 items by $s_i$; top 200 items go directly to full ranking.
• For the remaining 4800 items, compute interest score $s_i$ and diversity score $d_i$ for each item $i$.
• Rank remaining 4800 items by $s_i + d_i$; top 300 go to full ranking.

Retrieval Diversity

Two-Tower Model: Adding Noise

• The user tower takes user features as input and outputs a user vector representation; then ANN search retrieves items with high vector similarity.
• During online retrieval (after computing the user vector, before ANN search), add random noise to the user vector.
• The narrower the user's interests (e.g., if the user's recent $n$ interactions cover only a few categories), the stronger the noise added.
• Adding noise makes retrieved items more diverse and can improve core recommender system metrics.

Two-Tower Model: Sampling User Behavior Sequences

• The user's most recent $n$ interactions (user behavior sequence) are input to the user tower.
• Keep the most recent $r$ items ($r \ll n$).
• Randomly sample $t$ items from the remaining $n - r$ items ($t \ll n$). (Can be uniform sampling or non-uniform sampling for category balance.)
• Use the resulting $r + t$ items as the user behavior sequence instead of all $n$ items.
• Why does sampling the user behavior sequence improve metrics?
  • On one hand, injecting randomness makes retrieval results more diverse.
  • On the other hand, $n$ can be very large, capturing interests from much further in the user's history.

U2I2I: Sampling User Behavior Sequences

• In $U2I2I$ ($user \to item \to item$), the first $item$ refers to one of the user's most recent $n$ interactions, called a seed item in $U2I2I$.

• $n$ items cover relatively few categories with category imbalance.

  • The system has 200 categories; a given user's $n$ items may cover only 15 categories.
  • The football category accounts for $0.4n$ items; TV drama accounts for $0.2n$ items; other categories account for fewer than $0.05n$ each.

• Apply non-uniform random sampling to select $t$ items from $n$ items, achieving category balance. (Concept and effect are similar to sampling user behavior sequences in two-tower models.)

• Use the sampled $t$ items (instead of the original $n$ items) as $U2I2I$ seed items.

• On one hand, categories are more balanced, improving diversity. On the other hand, $n$ can be larger, covering more categories.

Exploration Traffic

• 2% of each user's exposed items are non-personalized, used for interest exploration.
• Maintain a curated content pool of high-quality items with high interaction metrics. (Content pool can be segmented by user group, e.g., males aged 30–40.)
• Randomly sample a few items from the curated pool, skip ranking, and directly insert them into the final ranked results.
• Interest exploration negatively impacts core metrics in the short term but has positive long-term effects.

Summary: Improving Diversity

• Full ranking: Combine interest score and diversity score for ranking; apply rule-based diversification.
• Pre-ranking: Use only interest score to select some items; use combined interest score and diversity score to select additional items.
• Retrieval: Add noise to two-tower user vectors; apply non-uniform random sampling to user behavior sequences (applicable to both two-tower and U2I2I).
• Interest exploration: Reserve a small fraction of traffic for non-personalized recommendation.

Improving Metrics: Special Treatment for Special User Groups

Why Special Treatment for Special User Groups?

1. New users and low-activity users have little behavioral history; personalized recommendation is inaccurate.
2. New users and low-activity users are prone to churn; measures must be taken to improve retention.
3. Special users' behavior (e.g., CTR, interaction rate) differs from mainstream users; models trained on all users' behavior are biased for special user groups.

Methods to Improve Metrics

1. Build special content pools for retrieval targeting special user groups.
2. Apply special ranking strategies to protect special users.
3. Apply special ranking models to eliminate bias in model predictions.

Building Special Content Pools

Special Content Pools

• Why special content pools?
• New users and low-activity users have little behavioral history; personalized retrieval is inaccurate. (Since personalization is poor, at least ensure content quality is good.)
• Build special content pools tailored to specific groups to improve user satisfaction. For example, for middle-aged women who like commenting, build a comment-promoting content pool to meet these users' interaction needs.

How to Build Special Content Pools

• Method 1: Select high-quality items based on interaction counts and interaction rates received by items.
  • Target segment: consider only a specific group, e.g., males aged 18–25 in second-tier cities.
  • Build content pool: score items using this group's interaction counts and interaction rates; select top-scoring items for the content pool.
  • The content pool has a weak personalization effect.
  • Content pool is updated periodically: add new items, remove items with low interaction rates or expired relevance.
  • This content pool only applies to that specific group.
• Method 2: Apply causal inference to assess items' contribution to group retention rate; select items based on their contribution.

Retrieval from Special Content Pools

• Typically use two-tower models to retrieve from special content pools.

  • Two-tower models are personalized.
  • For new users, two-tower personalization is inaccurate.
  • Compensate with high-quality content and weak personalization.

• Additional training cost?

  • For regular users, regardless of how many content pools there are, only one two-tower model is trained.
  • For new users, since there is very little interaction history, a separate model must be trained.

• Additional inference cost?

  • Content pools update periodically, requiring ANN index updates.
  • Online retrieval requires ANN search.
  • Special content pools are much smaller (10–100× smaller than the full content pool), so additional compute is minimal.

Special Ranking Strategies

Differentiated Ranking Models

• Special user groups behave differently from regular users. New users and low-activity users have CTR and interaction rates that are higher or lower than average.

• Ranking models are dominated by mainstream users and make inaccurate predictions for special users.

  • Models trained on all users' data make severely biased predictions for new users.
  • If 90% of an app's users are female, models trained on all users' data are biased for male users.

• Problem: For special users, how to make ranking model predictions more accurate?

• Method 1: Large model + small model.

  • Train a large model on all users' behavior; the large model's prediction $p$ fits user behavior $y$.
  • Train a small model on special users' behavior; the small model's prediction $q$ fits the large model's residual $y - p$.
  • For mainstream users, use only the large model's prediction $p$.
  • For special users, combine large and small model predictions: $p + q$.

• Method 2: Fuse multiple experts, similar to MMoE.

  • Use a single model with multiple experts, each outputting a vector.
  • Compute a weighted average of expert outputs.
  • Compute weights based on user features.
  • For new users, the model takes user features like recency and activity level as input and outputs weights for expert aggregation.

• Method 3: After large model prediction, calibrate with a small model.

  • Use the large model to estimate CTR and interaction rates.
  • Feed user features and large model CTR/interaction rate estimates into a small model (e.g., GBDT).
  • Train the small model on special user group data; small model output fits users' true behavior.

Wrong Approach

• Use one ranking model per user group; recommendation system maintains multiple large models simultaneously.

  • One main model; each user group has its own model.
  • Update the main model nightly with all users' data.
  • Based on the trained main model, retrain for 1 epoch on a specific group's data to create that group's model.

• Short-term metric improvement; high maintenance cost, harmful long-term.

  • Initially, the low-activity male user model has 0.2% higher AUC than the main model.
  • After several main model iterations, AUC cumulatively improves by 0.5%.
  • Too many special group models, unmaintained and unupdated long-term.
  • If the low-activity male user model is taken offline and replaced with the main model, AUC for low-activity male users actually improves by 0.3%!

Summary: Special Treatment for Special User Groups

• Retrieval: For special user groups, build special content pools and add corresponding retrieval channels.
• Ranking strategy: Exclude low-quality items to protect new users and low-activity users; use special fusion formulas for special user groups.
• Ranking model: Combine large and small models, with the small model fitting the large model's residual; use a single model with multiple experts; calibrate large model predictions with a small model.

Improving Metrics: Leveraging Interaction Behaviors

Follows

Value of Follow Count for Retention

• For a user, the more authors they follow, the stronger the platform's pull on them.

• User retention rate ($r$) is positively correlated with number of authors followed ($f$).

• If a user's $f$ is small, the recommender system should encourage them to follow more authors.

• How to use follow relationships to improve user retention?

• Method 1: Use ranking strategies to increase follow counts.

  • For user $u$, the model estimates the follow rate $p_i$ for candidate item $i$.
  • Let user $u$ have already followed $f$ authors.
  • Define a monotonically decreasing function $w(f)$: the more authors already followed, the smaller $w(f)$.
  • Add $w(f) \cdot p_i$ to the ranking fusion formula to encourage follows. (If $f$ is small and $p_i$ is large, $w(f) \cdot p_i$ gives item $i$ a large bonus.)

• Method 2: Build a follow-promoting content pool and retrieval channel.

  • Items in this pool have high follow rates and can promote follows.
  • If a user's follow count $f$ is small, apply this pool to that user.
  • Retrieval quota can be fixed or negatively correlated with $f$.

Value of Fan Count for Incentivizing Publishing

• UGC platforms treat author publishing volume and publishing rate as core metrics, hoping authors publish more.

• Items published by authors are pushed to users, generating likes, comments, follows, and other interactions.

• Interactions (especially follows and comments) improve authors' publishing motivation.

• The fewer fans an author has, the more each new fan boosts their publishing motivation.

• Use ranking strategies to help low-fan new authors gain fans.

• Let author $a$'s fan count (number of followers) be $f_a$.

• Author $a$'s item $i$ may be recommended to user $u$; the model estimates follow rate $p_{ui}$.

• Define a monotonically decreasing function $w(f_a)$ as weight; the more fans author $a$ has, the smaller $w(f_a)$.

• Add $w(f_a) \cdot p_{ui}$ to the ranking fusion formula to help low-fan authors gain fans.

Implicit Follow Relationships

• Retrieval channel U2A2I: user → author → item.
• Explicit follow relationships: User $u$ follows author $a$; recommend $a$'s published items to $u$. (CTR and interaction rate are typically higher than other retrieval channels.)
• Implicit follow relationships: User $u$ enjoys watching author $a$'s items but has not followed $a$.
• The number of implicitly followed authors far exceeds explicitly followed authors. Mining implicit follow relationships and building U2A2I retrieval channels can improve core recommender system metrics.

Shares

Promoting Shares (Share-back Traffic)

• A platform-A user shares an item to platform B, attracting off-platform traffic to A.
• Recommender systems that promote shares (also called share-back traffic) can improve DAU and consumption metrics.
• Does simply increasing share count work?
  • The model estimates share rate $p$; the fusion formula contains a term $w \cdot p$, giving items with high share rates more exposure.
  • Increasing weight $w$ promotes shares, attracting off-platform traffic, but negatively impacts CTR and other interaction rates.

KOL Modeling

• Goal: Attract as much off-platform traffic as possible without harming clicks and other interactions.

• Whose shares attract large off-platform traffic? Key Opinion Leaders (KOLs) on other platforms!

• How to determine if a user on our platform is a KOL on other platforms?

• Look at how much off-platform traffic their historical shares have driven.

• Method 2: Build a share-promoting content pool and retrieval channel, effective for off-platform KOLs.

Comments

Comments Promote Publishing

• UGC platforms treat author publishing volume and publishing rate as core metrics, hoping authors publish more.
• Interactions like follows and comments can improve authors' publishing motivation.
• If a newly published item has not received many comments yet, boost its estimated comment rate so the item gains comments quickly.
• Add an extra term $w_i \cdot p_i$ to the ranking fusion formula.
  • $w_i$: weight, negatively correlated with the number of comments item $i$ already has.
  • $p_i$: the model's estimated comment rate for recommending item $i$ to the user.

Other Value of Comments

• Some users enjoy writing comments and interacting with authors and other commenters.

  • Add a comment-promoting content pool for these users, giving them more opportunities to participate in discussions.
  • Helps improve these users' retention.

• Some users regularly leave high-quality comments (with high like counts on their comments).

  • High-quality comments contribute to retention of authors and other users. (Authors and other users find such comments interesting or helpful.)
  • Use ranking and retrieval strategies to encourage these users to comment more.

Summary: Leveraging Interaction Behaviors

• Follows:

  • Retention value (encourage new users to follow more authors, improving new user retention).
  • Publishing value (help new authors gain more fans, improving author publishing motivation).
  • Use implicit follow relationships for retrieval.

• Shares: Identify which users are off-platform KOLs; leverage the value of their shares to attract off-platform traffic.

• Comments:

  • Publishing value (encourage new items to receive comments, improving author publishing motivation).
  • Retention value (create more commenting opportunities for discussion-loving users).
  • Encourage high-quality commenters to comment more.

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