Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models
This incremental improvement addresses efficiency and performance issues for long-lived recommender systems in production environments.
The paper tackles the challenge of incorporating lengthy user-item interaction histories into large recommender system foundation models without increasing input dimensions, by introducing a sliding window training technique that improves learning of long-term user preferences and catalog item quality.
Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys foundation models (FM) need to encode this information in pretraining. Usually, this is done by either generating a long enough sequence length to take all history sequences as input at the cost of large model input dimension or by dropping some parts of the user history to accommodate model size and latency requirements on the production serving side. In this paper, we introduce a sliding window training technique to incorporate long user history sequences during training time without increasing the model input dimension. We show the quantitative & qualitative improvements this technique brings to the RecSys FM in learning user long term preferences. We additionally show that the average quality of items in the catalog learnt in pretraining also improves.