Multi-objective Evolutionary Rank Aggregation for Recommender Systems

Recommender systems help users to overcome the information overload problem by selecting relevant items according to their preferences. This paper deals with the problem of rank aggregation in recommender systems, where we want to generate a single consensus ranking from a given set of input rankings generated by different recommendation algorithms. This problem is NP-hard, and hence the use of meta-heuristics to solve it is appealing. Although accurate suggestions are mandatory for effective recommender systems, other recommendation quality measures need to be taken into account for delivering high-quality suggestions. This paper proposes Multi-objective Evolutionary Rank Aggregation (MERA), a genetic programming algorithm following the concepts of SPEA2 that considers three measures when suggesting items to users, namely mean average precision, diversity, and novelty. The method was tested in 3 realworld recommendation datasets, and the results show MERA can indeed find a balance for these metrics while generating a diverse set of solutions to the problem. MERA was able to return solutions with improvements of up to 15% in diversity (for the Movielens 1M dataset) and 7% in novelty (for the Filmtrust dataset) while maintaining, or even improving, the values of precision.