A novel multi-objective evolutionary algorithm for recommendation systems

Cui, Laizhong; Ou, Peng; Fu, Xianghua; Wen, Zhenkun; Lu, Nan

doi:10.1016/j.jpdc.2016.10.014

Cited by 59 publications

(45 citation statements)

References 30 publications

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“…Adomavicius and Tozhilin 5 classified RSs based on the techniques the system used to make meaningful recommendations. They are collaborative filtering, content-based filtering, and hybrid-based filtering that combines the two techniques in different ways.However, as most of the existing RSs used a single rating to represent the opinion of the user, current research has also confirmed that users' preferences for items may depend on several characteristics, which need to be taken into consideration while making recommendations 44 , 11 , 18 . Therefore, one of the most outstanding issues in the RSs research community is to overcome the limitations of using just one rating technique to recommend items 4 .…”

Section: Introductionmentioning

confidence: 70%

“…It predicts any missing rating by computing the average deviation in rating Δ i, j between any pair of items i and j in only two steps: pre-computation and rating prediction steps. Pre-computation step computes the value of Δ i, j using (11), where N i, j is the number of users who rated i and j. Finally, the unknown rating of item k by the user u is predicted from (12).…”

Section: Overall Rating Predictionmentioning

confidence: 99%

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Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Hassan¹,

Hamada²

2018

IJCIS

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We often make decisions on the things we like, dislike, or even don't care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user's preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms' methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

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Section: Introductionmentioning

confidence: 70%

Section: Overall Rating Predictionmentioning

confidence: 99%

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Hassan¹,

Hamada²

2018

IJCIS

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“…(4) The experiments on two classical data sets (Movielens and Netflix) show the superiority of our MOEA-EPG over the current MOEAs [30,33] in terms of accuracy, diversity, and novelty.…”

Section: Introductionmentioning

confidence: 93%

“…Probabilistic MOEA (PMOEA) [33]: This algorithm mainly introduced a new diversity indicator and a multiparent probability crossover to have a better recommendation. PMOEA was validated to obtain a good balance between the two objectives (accuracy and diversity) in recommendations.…”

Section: Multiobjective Optimization Problemsmentioning

confidence: 99%

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Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Lin

Wang

et al. 2018

Complexity

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Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.

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Multicriteria recommendation based on bacterial foraging optimization

Geng

Wang

et al. 2021

Int J of Intelligent Sys

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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.

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A novel multi-objective evolutionary algorithm for recommendation systems

Cited by 59 publications

References 30 publications

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Multicriteria recommendation based on bacterial foraging optimization

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