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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Selecting Sequences of Items via Submodular Maximization
Authors: Sebastian Tschiatschek, Adish Singla, Andreas Krause
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm in synthetic and real world experiments on a movie recommendation dataset. |
| Researcher Affiliation | Academia | Sebastian Tschiatschek ETH Zurich EMAIL Adish Singla ETH Zurich EMAIL Andreas Krause ETH Zurich EMAIL |
| Pseudocode | Yes | Algorithm 1 GREEDY: Node-based Greedy Algorithm; Algorithm 2 OMEGA: Edge-based Greedy Algorithm with Reordering; Algorithm 3 REORDER: Compute Sequence of Items from Set of Edges |
| Open Source Code | No | The paper refers to an 'extended version' for proofs, but does not provide any specific statement or link for the availability of source code for the described methodology. |
| Open Datasets | Yes | We performed real world movie recommendation experiments on the Movielens 1M dataset2. This dataset contains 1,000,209 ratings of 6,040 users for 3,706 movies. [...] 2http://grouplens.org/datasets/movielens/1m/ |
| Dataset Splits | No | The paper states, 'We randomly partitioned the data D into training data Dtrain and testing data Dtest such that |Dtest| = 500,' but does not specify details about a validation split or other specific partitioning methodologies. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with versions). |
| Experiment Setup | Yes | We model dependencies between the last z items in σpr and the items that can be selected, where we used z {1, 2, 5, } in our experiments (z = means that all items in σpr are considered). |