## List of available projects## Projects for the AUI Technology course (5 credit projects)Student taking this projects will take full credits from the AUI Technology course. ## T.1 Implementation of recommender algorithmsStudents will be required to implement a new recommender algorithm based on a high-level description from a research paper. The algorithm will be implemented in C++ in such a way that the algorithm can be integrated with the LensKit testing environment. Detailed instructions on how to perform this integration will be published later on this page. If the project is taken by a group of students, the same algorithm must be implemented also in Julia language. - T.1.a: Sparse Linear Methods with Side Information for Top-N Recommendations - Section 5.1
- T.1.b: TV Show Recommender Systems with Temporal Dynamics - Section 6.1
- T.1.c: Contextual Recommendation in Multi-User Devices
- T.1.d: Augmented Matrix Factorization with Explicit Labels for Recommender Systems
- T.1.e: Moving beyond Linearity and Independence in Top-N Recommender Systems - Section 3
- T.1.f: Fast ALS-based Matrix Factorization for Explicit and Implicit Feedback Datasets
- T.1.g: Matching users and items across domains to improve the recommendation quality
## Projects in cooperation between the AUI Interaction and AUI Technology courses |

### Projects

Subpages (1):
10 credits projects