Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Lin, Qiuzhen; Wang, Xiaozhou; Hu, Bishan; Ma, Lijia; Chen, Fei; Li, Jianqiang; Coello, Carlos A. Coello

doi:10.1155/2018/1716352

Cited by 27 publications

(16 citation statements)

References 44 publications

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“…e second is the problem of algorithm selection as there are many algorithms, and it is not clear which one should be chosen. is is the core of personalized recommendation because different algorithms may produce different recommendations [14,15]. erefore, before evaluating any recommendation system, the first thing to evaluate is its recommendation algorithm.…”

Section: Related Personalized Recommendation Algorithmmentioning

confidence: 99%

Communication-Based Book Recommendation in Computational Social Systems

Zuo

Xiong

et al. 2021

Complexity

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This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.

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Section: Related Personalized Recommendation Algorithmmentioning

confidence: 99%

Communication-Based Book Recommendation in Computational Social Systems

Zuo

Xiong

et al. 2021

Complexity

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“…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. Lin et al [31] present a multi-objective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). In MOEA-EPG, the accuracy, diversity, and novelty of recommendations are chosen as the three conflicting objectives, and the aim of that algorithm is to optimize the modeled MOP for personalized recommendation.…”

Section: Performance Analysis In Terms Of Accuracy (Precision and mentioning

confidence: 99%

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Cardenas

Calisaya²,

Martinez-Salinas

et al. 2020

IJACSA

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In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

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“…After that, an offspring o is generated using the recombination operators defined in (6)-(9) based on p i and the mating parents in line (10) and is further evaluated to get the objective values in line (11), which is used to update the approximately ideal point * in (2). In line (12), this offspring o i is added into the offspring population O.…”

Section: Moea/d-csumentioning

confidence: 99%

“…In real-world applications, it is often needed to handle multiobjective optimization problems (MOPs) [1], such as recommendation systems [2,3], privacy computing [4], and resource assignment [5][6][7]. Due to the conflicts among different objectives, the results of MOPs will output a set of Pareto solutions (PS) and their mapping in the objective space is called Pareto front (PF) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition

Lin

Wang

et al. 2019

Complexity

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This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. Different from the existing approaches that assign one solution to each agent, our approach allocates the closest solutions to each agent and thus the number of solutions in an agent may be zero and no less than one. Regarding the agent with no solution, it will be assigned one solution in priority, once offspring are generated closest to its subproblem. To keep the same population size, the agent with the largest number of solutions will remove one solution showing the worst convergence. This improves diversity for one agent, while the convergence of other agents is not lowered. On the agent with no less than one solution, offspring assigned to this agent are only allowed to update its original solutions. Thus, the convergence of this agent is enhanced, while the diversity of other agents will not be affected. After a period of evolution, our approach may gradually reach a stable status for solution assignment; i.e., each agent is only assigned with one solution. When compared to six competitive multiobjective evolutionary algorithms with different population selection or update strategies, the experiments validated the advantages of our approach on tackling two sets of test problems.

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Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Cited by 27 publications

References 44 publications

Communication-Based Book Recommendation in Computational Social Systems

Communication-Based Book Recommendation in Computational Social Systems

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition

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