Selecting Sequences of Items via Submodular Maximization
Authors: Sebastian Tschiatschek, Adish Singla, Andreas Krause
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 sebastian.tschiatschek@inf.ethz.ch Adish Singla ETH Zurich adish.singla@inf.ethz.ch Andreas Krause ETH Zurich krausea@ethz.ch |
| 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). |