Offline optimization for user-specific hybrid recommender systems

Abstract

Massive availability of multimedia content has given rise to numerous recommendation algorithms that tackle the associated information overload problem. Because of their growing popularity, selecting the best one is becoming an overload problem in itself. Hybrid algorithms, combining multiple individual algorithms, offer a solution, but often require manual configuration and power only a few individual recommendation algorithms. In this work, we regard the problem of configuring hybrid recommenders as an optimization problem that can be trained in an offline context. Focusing on the switching and weighted hybridization techniques, we compare and evaluate the resulting performance boosts for hybrid configurations of up to 10 individual algorithms. Results showed significant improvement and robustness for the weighted hybridization strategy which seems promising for future self-adapting, user-specific hybrid recommender systems.

Online optimization for user-specific hybrid recommender systems

“…In previous work [16], which focused on offline optimization, we evaluated the quality of our hybridization approach and experimented with different optimization techniques. In this work, since focus is on online optimization, we evaluate the scalability and performance of the system.…”

“…This work builds on our previous work [16] where we focused on offline optimization for user-specific hybrid recommender systems. Different hybrid configurations (combining up to 10 individual recommendation algorithms) were tested and their optimization performance was evaluated.…”

“…In this section we continue from previous work [16] where an offline optimization procedure for a self-learning hybrid recommender was defined. We propose an architecture for an online environment taking into account the defined requirements 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 for online recommender systems.…”

“…In our previous work [16] we illustrated the offline optimization procedure using binary search and RMSE as evaluation criteria. The optimization method can be any chosen method as long as it has a fast convergence rate to the optimal value.…”

“…Assuming every user is different and given all recommendation algorithms, each user may be best served by a different algorithm, or even a different combination of algorithms (i.e., hybrid). In previous work [16] we pursued the ideal hybrid recommender which would be capable of integrating all known recommendation algorithms and auto-adapting its hybrid configuration to dynamically generate optimal recommendations for every user specifically. A hybrid recommendation strategy was devised capable of dynamically optimizing its configuration.…”