SQL-Rank: A Listwise Approach to Collaborative Ranking
Authors: Liwei Wu, Cho-Jui Hsieh, James Sharpnack
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare our proposed algorithm (SQL-Rank) with other state-of-the-art algorithms on real world datasets. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of California, Davis, CA, USA 2Department of Computer Science, University of California, Davis, CA, USA. |
| Pseudocode | Yes | Algorithm 1 SQL-Rank: General Framework |
| Open Source Code | Yes | SQL-Rank: our proposed algorithm implemented in Julia 1. 1https://github.com/wuliwei9278/SQL-Rank |
| Open Datasets | Yes | We experiment on the following four datasets. Note that the original data of Movielens1m, Amazon and Yahoo-music are ratings from 1 to 5, so we follow the procedure in (Rendle et al., 2009; Yu et al., 2017) to preprocess the data. ... Movielens1m: a popular movie recommendation data with 6, 040 users and 3, 952 items. Amazon: the Amazon purchase rating data for musical instruments 3 with 339, 232 users and 83, 047 items. Yahoo-music: the Yahoo music rating data set 4 which contains 15, 400 users and 1, 000 items. Foursquare: a location check-in data5. |
| Dataset Splits | Yes | We use rank r = 100 and tune regularization parameters for all three algorithms using a random sampled validation set. |
| Hardware Specification | Yes | All experiments are conducted on a server with an Intel Xeon E5-2640 2.40GHz CPU and 64G RAM. |
| Software Dependencies | No | The paper mentions 'Julia' and 'C++' but does not provide specific version numbers for these or other software dependencies, aside from an ambiguous 'Julia 1'. |
| Experiment Setup | Yes | We use rank r = 100 and tune regularization parameters for all three algorithms using a random sampled validation set. For Weighted-MF, we also tune the confidence weights on unobserved data. For BPR and SQL-Rank, we fix the ratio of subsampled unobserved 0 s versus observed 1 s to be 3 : 1, which gives the best performance for both BPR and SQL-rank in practice. |