Weinan Zhang, Rui Zhang, Bo Chen, Fei Sun, Ruiming Tang, Weiwen Liu, Yunjia Xi, Jiarui Qin
We lifted 11 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| librerank-community/librerank | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| attention_score | Ran | librerank-community/librerank/librerank/reranker.py code served (permissive licence) · get_code("54048768686697eb") |
| dcg | Ran | librerank-community/librerank/librerank/ranker.py code served (permissive licence) · get_code("004233e23c2f2f2c") |
| dcg_k | Ran | librerank-community/librerank/librerank/ranker.py code served (permissive licence) · get_code("089f79f16aa06ac6") |
| get_batch | Ran | librerank-community/librerank/librerank/utils.py code served (permissive licence) · get_code("63e6041e478da028") |
| ideal_dcg | Ran | librerank-community/librerank/librerank/ranker.py code served (permissive licence) · get_code("b6b3d40dcf81ad23") |
| normalize | Ran | librerank-community/librerank/librerank/utils.py code served (permissive licence) · get_code("0c7edb5c1b126003") |
| softmax | Ran | librerank-community/librerank/librerank/utils.py code served (permissive licence) · get_code("b6122f021db6d387") |
| tau_function | Ran | librerank-community/librerank/librerank/reranker.py code served (permissive licence) · get_code("b740afcce16e01a8") |
| eval | Not yet run | librerank-community/librerank/run_init_ranker.py code served (permissive licence) · get_code("255560f376bfc7d2") |
| eval | Not yet run | librerank-community/librerank/run_reranker.py code served (permissive licence) · get_code("c35a93aba4f12ded") |
| get_data | Not yet run | librerank-community/librerank/run_init_ranker.py code served (permissive licence) · get_code("945eae94ea323225") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects users' experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely adopted in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide a benchmark for the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/ LibRerank.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2202.06602")
get_code_for_paper("2202.06602")
have("2202.06602")
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