Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Learning User Preferences to Incentivize Exploration in the Sharing Economy
Authors: Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb. Our ๏ฌndings suggest that our approach is well-suited to learn appropriate incentives and increase exploration on the investigated platform. |
| Researcher Affiliation | Collaboration | Christoph Hirnschall ETH Zurich Zurich, Switzerland EMAIL Adish Singla MPI-SWS Saarbr ucken, Germany EMAIL Sebastian Tschiatschek Microsoft Research* Cambridge, United Kingdom EMAIL Andreas Krause ETH Zurich Zurich, Switzerland EMAIL *Work performed while at ETH Zurich. |
| Pseudocode | Yes | Algorithm 1: OL Online Learning; Algorithm 2: Co OL Coordinated Online Learning; Function 3: AProj Approximate Projection |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology or provide a link to a code repository. |
| Open Datasets | Yes | Airbnb dataset. Using data of Airbnb apartments from insideairbnb.com, we created a dataset of 20 apartments as follows: we chose apartments from 4 types in New York City by location (Manhattan or Brooklyn) and number of reviews (high, 20 or low, 2). (1Data from insideairbnb.com.) |
| Dataset Splits | No | The paper does not specify explicit training, validation, and test dataset splits (e.g., percentages, sample counts, or predefined split references). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., libraries, frameworks, or solvers) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Utility gain. The utility gain u for getting a review for infrequently reviewed apartments is set to u = 40 in our experiments, based on referral discounts given by Airbnb in the past. Loss function. ...we use a delta value of 20. Structure. Due to the small number of apartments, we consider a non-contextual setting with d = 1 and use an rbounded hemimetric structure to model the relationship of the tasks, where r is set to 40 to avoid recommending incentives pt > u. |