A Two-phase Evolutionary Algorithm for Solving the Accuracy-diversity Dilemma in Recommendation

Zhipeng Hou, Jing Liu

Abstract: Generating both accurate and diverse recommendations is required for modern recommender systems. Accurate recommendations can well meet the needs of users, while diverse recommendations can bring users novelty, discover the unknown preferences of users, and can also alleviate the long tail problem. However, these two metrics are conflicting and it is very hard to improve both at the same time. Thus, a compromise needs to be made. The easiest way is using hyperparameters to combine different objective functions or the recommendations of different recommendation algorithms, but it is difficult to determine parameters. Another common strategy is applying multi-objective evolutionary algorithm to provide multiple recommendations for each user, but how to choose the final recommendation becomes a new problem. To this end, we propose a two-phase evolutionary algorithm-based recommendation framework, named EARF. In EARF, a novel fitness function is designed, which converts the evaluation of accuracy and diversity into a single objective and automatically trades off these two goals. Based on the property of recommendation problem and fitness function, the genetic representation and operators are redefined. The experiments on real-world rating datasets indicate that the EARF is effective and the proposed evolutionary algorithm can achieve a good balance between accuracy and diversity.