OpenP5: Benchmarking Foundation Models for Recommendation

This paper presents OpenP5, an open-source library for benchmarking foundation models for recommendation under the Pre-train, Personalized Prompt and Predict Paradigm (P5). We consider the implementation of P5 on three dimensions: 1) downstream task, 2) recommendation dataset, and 3) item indexing method. For 1), we provide implementation over two downstream tasks: sequential recommendation and straightforward recommendation. For 2), we surveyed frequently used datasets in recommender system research in recent years and provide implementation on ten datasets. In particular, we provide both single-dataset implementation and the corresponding checkpoints (P5) and another Super P5 (SP5) implementation that is pre-trained on all of the datasets, which supports recommendation across various domains with one model. For 3), we provide implementation of three item indexing methods to create item IDs: random indexing, sequential indexing, and collaborative indexing. We also provide comprehensive evaluation results of the library over the two downstream tasks, the ten datasets, and the three item indexing methods to facilitate reproducibility and future research. We open-source the code and the pre-trained checkpoints of the OpenP5 library at https://github.com/agiresearch/OpenP5.