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..
Optimal Greedy Diversity for Recommendation
Authors: Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, Zheng Wen
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach in an of๏ฌine analysis, which incorporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods. |
| Researcher Affiliation | Industry | Azin Ashkan Technicolor Research, USA EMAIL Branislav Kveton Adobe Research, USA EMAIL Shlomo Berkovsky CSIRO, Australia EMAIL Zheng Wen Yahoo! Labs, USA EMAIL |
| Pseudocode | Yes | Algorithm 2 DUM: Diversity-Weighted Utility Maximization |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We use the 1M Movie Lens dataset1, which consists of movie ratings given on a 1-to-5 stars scale. 1http://www.grouplens.org/node/12 |
| Dataset Splits | No | The paper mentions splitting data into training and test sets but does not specify a validation set split. The term "validation" is used in the context of general empirical evidence, not a dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in the experiments. |
| Experiment Setup | Yes | For each user, we set Nt = rt K in (5), where K is the length of the recommendation list and rt is the user s preference score for genre t . That is, the coverage of a genre in the list is proportional to the degree of user preference for the genre. ... We compare DUM to three variants of MMR, which are parameterized by ฮป 1/3, 0.99 . |