Argumentation-Based Recommendations: Fantastic Explanations and How to Find Them
Authors: Antonio Rago, Oana Cocarascu, Francesca Toni
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that our method is competitive in generating correct predictions, compared with state-of-the-art methods |
| Researcher Affiliation | Academia | Antonio Rago, Oana Cocarascu, Francesca Toni Department of Computing, Imperial College London, UK {a.rago15, oc511, ft}@imperial.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate experimentally the A-I recommender System on a movie dataset extracted from the Netflix dataset2 and imdbapi3. |
| Dataset Splits | Yes | Further, in our experiments we vary the number of users in the training set, considering all users who have rated at least 10 movies or, alternatively, 20 movies, and make predictions for users who have rated fewer than 10 or 20 movies, respectively (in other words these users are part of our test set). To address the cold-start problem, our training sets also include 5, 7 or 10 movies for users who have rated fewer than 10 or 20 movies, with these (5, 7 or 10) movies not included in the test sets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments are provided. |
| Software Dependencies | No | The paper mentions 'Suprise library [Hug, 2017]' but does not provide a specific version number for the software dependency. |
| Experiment Setup | Yes | In our experiments, we use the following constants for the profile of all users u U: µu c.f. = 0.3, µu genre = 0.3, µu actor = 0.5, µu director = 0.2, and, to determine the similarity constants between any two (different) users, we use the cosine distance between the users preferences for all aspects of type genre. |