We lifted 2 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| rlqja1107/melt | canonical | 1 of 1 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| swish | Ran | this paper's copy was not recorded; identical code first harvested from jeonsworld/ViT-pytorch pointer only · get_code("0f786c407fb1ee4c") |
| gelu | Ran | rlqja1107/melt/models/MELT_FMLP.py pointer only (licence: NONE) · get_code("56a9ab06b860b180") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studies address the long-tailed problem in SRS, they only focus on either the user or item perspective. However, we discover that the long-tailed user and item problems exist at the same time, and considering only either one of them leads to sub-optimal performance of the other one. In this paper, we propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT), that jointly alleviates the long-tailed problem in the perspectives of both users and items. MELT consists of bilateral branches each of which is responsible for long-tailed users and items, respectively, and the branches are trained to mutually enhance each other, which is trained effectively by a curriculum learning-based training. MELT is model-agnostic in that it can be seamlessly integrated with existing SRS models. Extensive experiments on eight datasets demonstrate the benefit of alleviating the long-tailed problems in terms of both users and items even without sacrificing the performance of head users and items, which has not been achieved by existing methods. To the best of our knowledge, MELT is the first work that jointly alleviates the long-tailed user and item problems in SRS.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2304.08382")
get_code_for_paper("2304.08382")
have("2304.08382")
Connect an agent — have() is free.