Online Reciprocal Recommendation with Theoretical Performance Guarantees
Authors: Fabio Vitale, Nikos Parotsidis, Claudio Gentile
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines. |
| Researcher Affiliation | Collaboration | Fabio Vitale Department of Computer Science Sapienza University of Rome (Italy) & University of Lille (France) & INRIA Lille Nord Europe Rome, Italy & Lille, France fabio.vitale@inria.frNikos Parotsidis University of Rome Tor Vergata, Rome, Italy nikos.parotsidis@uniroma2.itClaudio Gentile INRIA Lille & Google New York Lille, France & New York, USA cla.gentile@gmail.com |
| Pseudocode | Yes | Algorithm 1: OOMM (Oblivious Online Match Maker)Algorithm 2: SMILE (Sampling Matching Information Leaving out Exceptions) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | As for real-world datasets, we used the one from [4], which is also publicly available. This is a dataset from a Czech dating website, where 220,970 users rate each other in a scale from 1 (worst) to 10 (best). |
| Dataset Splits | No | The paper describes the creation of specific dense subsets from a sparse dataset and measures performance over time horizons, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their exact versions). |
| Experiment Setup | Yes | The parameter S in SMILE has been set to S + S log n, with S = (n2 log n)/ ˆ M, where ˆ M is the estimate from Phase 0.We randomly partitioned B and G into CB and CG clusters, respectively. Each boy likes all the girls of a cluster C with probability 0.2, and with probability 0.8 dislikes them. We do the same for the preferences from girls to boy clusters. Finally, for each preference (either positive or negative) we reverse its sign with probability 1/(2 log n) (in our case, n = 2000). |