Multicriteria recommendation based on bacterial foraging optimization

Abstract

Recommender systems assist users to make decisions among a huge volume of options. Accuracy-oriented recommender systems focus on the prediction power of algorithms and neglect that users may appreciate diverse and novel recommendations in real-world scenarios. Thus, this paper proposed a multicriteria recommendation model that can optimize the recommendation accuracy, diversity, novelty, and individual tendency simultaneously.Additionally, a new multiobjective bacterial foraging optimization method is proposed to improve its searching capability and the performance of recommendation model. The proposed optimization-based multicriteria recommendation algorithm is compared with existing methods on both benchmark functions and real-world data sets. The results demonstrate that the proposed algorithm is superior to other recommendation algorithms in most cases. This study provides insights in recommendation system design and draws scholarly attention to the optimization-based recommendation strategy.

Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems

“…Calculating the weighted aggregation of multi-criteria ratings is one of the challenging tasks involved in producing recommendations for users. Therefore, different weight learning algorithms such as Genetic Programming (GP) [ 19 ], GA [ 20 , 22 ], particle swarm optimization [ 23 ], bacterial foraging optimization [ 24 ], etc. are used for learning the weights.…”