NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
arxiv(2023)
摘要
Large foundational models, through upstream pre-training and downstream
fine-tuning, have achieved immense success in the broad AI community due to
improved model performance and significant reductions in repetitive
engineering. By contrast, the transferable one-for-all models in the
recommender system field, referred to as TransRec, have made limited progress.
The development of TransRec has encountered multiple challenges, among which
the lack of large-scale, high-quality transfer learning recommendation dataset
and benchmark suites is one of the biggest obstacles. To this end, we introduce
NineRec, a TransRec dataset suite that comprises a large-scale source domain
recommendation dataset and nine diverse target domain recommendation datasets.
Each item in NineRec is accompanied by a descriptive text and a high-resolution
cover image. Leveraging NineRec, we enable the implementation of TransRec
models by learning from raw multimodal features instead of relying solely on
pre-extracted off-the-shelf features. Finally, we present robust TransRec
benchmark results with several classical network architectures, providing
valuable insights into the field. To facilitate further research, we will
release our code, datasets, benchmarks, and leaderboards at
https://github.com/westlake-repl/NineRec.
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