What is sampling bias in recommendation, and how to correct them
Recommendations are ubiquitous in our digital lives, ranging from e-commerce giants to streaming services. However, hidden beneath every large recommender system lies a challenge that can significantly impact their effectiveness — sampling bias.
In this article, I will introduce how sampling bias occurs during training recommendation models and how we can solve this issue in practice.
Let’s dive in!
In general, we can formulate the recommendation problem as follows: given query x (which can contain user information, context, previously clicked items, etc.), find the set of items {y1,.., yk} that the user will likely be interested in.
One of the main challenges for large-scale recommender systems is low-latency requirements. However, user and item pools are vast and dynamic, so scoring every candidate and greedily finding the best one is impossible. Therefore, to meet the latency requirement, recommender systems are generally broken down into 2 main stages: retrieval and ranking.
Retrieval is a cheap and efficient way to quickly capture the top item candidates (a few hundred) from the vast candidate pool (millions or billions). Retrieval optimization is mainly about 2 objectives:
- During the training phase, we want to encode users and items into embeddings that capture the user’s behaviour and preferences.
- During the inference, we want to quickly retrieve relevant items through Approximate Nearest Neighbors (ANN).
For the first objective, one of the common approaches is the two-tower neural networks. The model gained its popularity for tackling the cold-start problems by incorporating item content features.
In detail, queries and items are encoded by corresponding DNN towers so that the relevant (query, item) embeddings stay…
