Optimisation of combined collaborative recommender systems
A new approach to collaborative user modelling is presented in this paper. We have developed a framework that can be used for easy testing of different concepts. We have also introduced three different areas where collaborative modelling can be further improved. For the first phase of testing, we have created a hybrid system based on three different collaborative recommender techniques. Since this system implements multiple collaboration techniques, we decided to call this approach Combined Collaborative Recommender. Although each prediction technique can produce adequate results, we have proved that the combination of these techniques into a unified system provides a much more stable system. It should also be pointed out that these analyses were done using a very large dataset (more than 2 million ratings) providing reliable results. Results of these optimisations are presented along with pointers for further development.
collaborative recommendation user modelling personalisation hybrid recommender systems