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Contribution Details
| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | What Shall We Watch Tonight?: Why sometimes your favourite streaming service just cannot manage to recommend anything interesting |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | IEEE VIS Workshop on Visualization for AI Explainability |
| Event Type | workshop |
| Event Location | Oklahoma City, USA |
| Event Start Date | October 12 - 2022 |
| Event End Date | October 16 - 2022 |
| Publisher | IEEE |
| Abstract Text | If you have ever used an e-commerce service or a streaming platform, you have already come across something like: "recommended for you", or "other users have also bought this". Our educational article below will give you an introduction to Recommender Systems (RS), and illustrate how this field currently leverages deep-learning techniques. Our article is meant to foster in the reader a broad inuition of RS, highlight common scenarios causing failure in various RS techniques, and provide a visual understanding of how recommender systems work. First, we illustrate how Matrix Factorization (MF) works-- a (relatively) simply designed recommender system. Then, we gradually increase the sophistication of our approach, exploring the effect this has on the prediction accuracy of a model. We explain differences in two models' competence in predicting a user's rating of movies they have seen, through various visualizations. Our two models use the MovieLens 1M dataset, selecting different sets of features (columns) per model [18]. |
| Free access at | Official URL |
| Official URL | https://ibrahimalhazwani.github.io/distill-xai/ |
| Other Identification Number | merlin-id:23095 |
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