Program
The program of summer school consists of a series of lectures by Associate Professor Dmitry I. Ignatov on recommender systems.
Wednesday (May 29)
| 13:55 | - | 14:00 | Opening |
| 14:00 | - | 14:30 | Lecture 1: Intro to recommender systems. Non-personalised and content-based systems. |
| 14:30 | - | 15:30 | Lecture 2: Collaborative filtering (user-based and item-based). Similarity measures. Bimodal cross-validation. Practice in ipython. Similarity aggregation in collaborative filtering. |
| 15:30 | - | 16:00 | Refreshment |
| 16:00 | - | 17:00 | Lecture 3: Frequent itemset mining for web advertising and web audience exploration. |
Thursday (May 30)
| 9:00 | - | 9:30 | Breakfast |
| 9:30 | - | 10:30 | Lecture 4: BMF versus SVD for collaborative filtering. Context-aware recommender systems: incorporation of side-information. |
| 10:30 | - | 11:30 | Lecture 5: Advanced matrix factorisation. PureSVD, NMF, SVD++, timeSVD. Factorisation Machines. Practice in ipython. |
| 11:30 | - | 13:00 | Lunch (on everyones' own) |
| 13:00 | - | 13:45 | Lecture 6: Hybrid recommender systems. Radiostation recommendations. |
| 13:45 | - | 14:30 | Lecture 7: Quality evaluation. Ranking vs. rating prediction. Towards a proper metric for recommender algorithm: ImhoNet Case. |
| 14:30 | - | 15:30 | Lecture 8: Selected advanced topics: spectral clustering and deep learning based examples. |
| 15:30 | - | 16:00 | Refreshment |
| 16:00 | - | 18:00 | Workshop: Discussion, Q&A and PhD students' presentations |
| 19:00 | - | | Social event |
Links
