The Web Conference proceedings are out, and, as is often the case, one of our favorite papers is from Pinterest. Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations observes that ranking models often develop a bias toward established items and away from fresh (cold-start) items. Since there's a feedback loop between what is recommended and the logged user interactions that flow into model training and future recommendations, this bias can become self-reinforcing and perpetuate the under-recommendation of fresh items. As a result, recommendations can become stale and can detract from the users’ experience of serendipity.
We love that this paper takes a holistic view of recommendation quality that extends beyond predictive accuracy, identifies specific reasons for the bias toward warm established items, and offers very practical techniques to address each one. The adjustments are to the training data, model architecture, and loss function—all applied at training time—which avoids any additional serving complexity. They are also relatively straightforward to implement, without any new hyper-parameters that would require additional tuning.
1. Cold items are under-represented in logged data
By definition, cold items have few logged interactions, so training losses and gradients are naturally biased toward warm items. Rather than directly addressing this disparity (e.g. through data augmentation for cold items), the authors frame the problem as generalizing from warm to cold items and seek to address it by applying manifold mixup. This method linearly interpolates training examples and labels within each training batch, generating new training examples that are semantic blends of existing examples.
The authors suggest that this improves generalization by discouraging the model from learning trivial patterns using few embedding space dimensions. Since one of the most trivial patterns is prevalence or popularity in the training data, we can kind of squint and see how this might help with cold-start—and indeed, they show that there are practical benefits, even if it's not fully clear why it works.
2. Cold items lack the historical features that models heavily rely on
For items with sufficient user-engagement history, we'd expect that historical engagement features be more predictive than non-historical (e.g. content-based) features. Cold items lack that user-engagement history, so we'd like our model to use the non-historical features make plausible predictions for cold items to help them gain exposure. However, because cold items are under-represented in the training data, models aren't really discouraged from learning to emphasize historical features over everything else and neglecting non-historical features, which hurts predictions for cold items. Learning this shortcut is expedient for boosting overall predictive accuracy, but contributes to the problem of cold items being under-recommended.
The authors address this by allowing non-historical features to bypass their model's feature interaction layer and contribute directly to downstream predictions. They show that this encourages the model to put more weight on such features, benefiting cold-start items that don't have much engagement history yet.
3. Cold items tend to receive lower scores than warm items
Warm items have more user interactions (by definition), which means they appear more frequently as positive examples in training data. This makes it understandable that models tend to learn that warm items deserve higher relevance scores.
That pattern isn't what we want though, and the authors address this issue by adding a term to the loss function to directly regularize the gap between warm and cold item scores. They point out that this approach has significant practical advantages over previously studied methods, because it adds a minimal number of model parameters (<5%) and doesn't substantially increase either training time or serving latency, which makes it suitable for large-scale industrial recommenders.
By combining these three techniques, Pinterest was able to improve the offline hit rate for fresh cold-start items at only a minimal cost to the hit rate for all items. In an online A/B test, they found this increased engagement with lower-ranked items, which they attribute to improved generalization and avoidance of learning dominant patterns from the head of the item popularity distribution. More surprisingly, they also found that this increased engagement from less active, cold-start users. It's not completely clear why, but we speculate that fresher recommendations might be more engaging for new users.