We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Yu-Fangxu/DARec | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| check_positive | Ran | Yu-Fangxu/DARec/U-DArec/Train_AutoRec.py pointer only (licence: NONE) · get_code("f19605e219ca8630") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxilliary contents, e.g., images and review texts, and the patterns in the rating matrix itself is rarely touched. In this work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model (DARec) that is capable of extracting and transferring patterns from rating matrices {\em only} without relying on any auxillary information. We empirically demonstrate on public datasets that our method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1905.10760")
get_code_for_paper("1905.10760")
have("1905.10760")
Connect an agent — have() is free.