SAGA: A Submodular Greedy Algorithm for Group Recommendation
Authors: Shameem Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe the experimental setup and report the results using user groups generated from the 1M Movie Lens1 dataset. |
| Researcher Affiliation | Collaboration | Shameem A Puthiya Parambath QCRI, HBKU, Doha, Qatar spparambath@hbku.edu.qa Nishant Vijayakumar Apptopia Inc., Boston, USA nishant.vijayakumar@gmail.com Sanjay Chawla QCRI, HBKU, Doha, Qatar schawla@hbku.edu.qa |
| Pseudocode | Yes | Algorithm 1: SAGA: Submodular Greedy Group Recommendation Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | In this section, we describe the experimental setup and report the results using user groups generated from the 1M Movie Lens1 dataset. 1http://grouplens.org/datasets/movielens/ |
| Dataset Splits | No | We carried out holdout validation by randomly selecting 30% of the item set and marking it as unrated wherever the rating values are observed. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'non-negative matrix factorization based on weighted-regularized non-negative alternating least squares algorithm' but does not specify any software names with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | In our experiments, the dimension of the item/user feature space is set to 150. We used the rbf kernel as the item afinity function h, i.e. Wij = exp( γ||xi xj||2) where xi and xj are the ith and jth item features. The γ value is chosen by running the algorithm for a set of values in the range {2 3, , 23} in multiples of two, and the reported results are for the best γ value for the respective algorithms. For the item saturation function f, we use natural logarithm f(x) = ln(1 + x), and for the user saturation function we experimented with two settings (i) identity function gu(x) = x and (ii) concave function gu(x) = x. |