Fast Constrained Submodular Maximization: Personalized Data Summarization
Authors: Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate FANTOM on the three real-world applications we described in Section 4: personalized movie recommendation, personalized image summarization, and revenue maximization. The main goal of this section is to validate our theoretical results, and demonstrate the effectiveness of FANTOM in practical scenarios where existing algorithms are incapable of providing desirable solutions. |
| Researcher Affiliation | Collaboration | Baharan Mirzasoleiman BAHARANM@INF.ETHZ.CH ETH Zurich Ashwinkumar Badanidiyuru ASHWINKUMARBV@GOOGLE.COM Google Amin Karbasi AMIN.KARBASI@YALE.EDU Yale University |
| Pseudocode | Yes | Algorithm 1 GDT Greedy with density threshold and Algorithm 3 FANTOM |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided, nor does it include any links to a code repository. |
| Open Datasets | Yes | Our personalized recommendation experiment involves FANTOM applied to a set of 10,437 movies from the Movie Lens ratings database [Mov, 2015]. We performed our experiments on a set of 10,000 Tiny Images [Krizhevsky and Hinton, 2009]. |
| Dataset Splits | No | The paper describes the datasets used (Movie Lens, You Tube social network, Tiny Images) but does not provide explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library names with version numbers used for the implementation or experiments. |
| Experiment Setup | Yes | Fig. 1a compares the performance of our approach to the benchmarks using Eq. 2 with λ = 1 for three genres: adventure, animation, and fantasy. A total of l = 19 uniform matroid constraints are considered to limit the number of movies chosen from each of the 19 genres. The limits for all the matroid constraints are set to 3. We also considered an additional uniform matroid constraint to restrict the size of the final solution to 10 movies. Moreover, a knapsack constraint is considered to model the total available budget. We chose the parameter λ = 0.2 to scale the costs to the interval [0, 1]. To model different characteristics of the products, we model the revenues by the concave function vq i (S) = αq q P j S wi,j, where αq depends on the type of the product. |