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 [1].
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 . |