NNIA-RS: A multi-objective optimization based recommender system

Geng, Bingrui; Li, Lingling; Jiao, Licheng; Gong, Maoguo; Cai, Qing; Wu, Yue

doi:10.1016/j.physa.2015.01.007

Cited by 54 publications

(31 citation statements)

References 30 publications

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“…For the target user, R is the list of items recommended for user u. Just like other methods [31], [32], we use function f 1 to define the accuracy of the recommendation list as follows. (u, i) denotes the similarity between the user u and item i.…”

Section: Multi-objective Recommendation System Formulationmentioning

confidence: 99%

“…Encoding is a key technique to immune algorithm. different from Geng et al [31], we use real encoding. in our algorithm, each solution represents one recommendation list.…”

Section: ) Key Techniques Of Moia Based Recommendation A: Encoding Omentioning

confidence: 99%

“…Then SPEA-2 was used to find a sequence of weights for each item. Geng et al [31] proposed an NNIAbased recommendation system, which was to improve the diversity of recommendation list. Zuo et al [32] presented a MOEA-based recommendation system by optimizing the accuracy and diversity at the same time and got a better result.…”

Section: Introductionmentioning

confidence: 99%

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Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Chai

Han

et al. 2019

IEEE Access

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Recommendation system plays a significant role in helping people to get effective information from mass data. Traditional recommendation systems focus on the recommendation accuracy, which is not sufficient. In this paper, we also consider the various needs of users to achieve more diverse recommendation. However, accuracy and diversity are two conflicting goals for recommendation system. Hence, we model the recommendation system as a multi-objective optimization problem, and aim to find tradeoff solutions between the two goals. Because the rating matrix is rather sparser in recommendation, we first use singular value decomposition to get the recommendation list, then multi-objective immune algorithm is used to optimize it. The experimental results illustrate that the proposed algorithm can get more diverse and accurate recommendation results.

show abstract

Section: Multi-objective Recommendation System Formulationmentioning

confidence: 99%

“…Encoding is a key technique to immune algorithm. different from Geng et al [31], we use real encoding. in our algorithm, each solution represents one recommendation list.…”

Section: ) Key Techniques Of Moia Based Recommendation A: Encoding Omentioning

confidence: 99%

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Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Chai

Han

et al. 2019

IEEE Access

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“…In MOPs, the optimization of one objective would affect that of the others. Thus, the result of optimizing all the objectives is not a single optimal solution, but a set of solutions, each of which is the best tradeoff among all the objectives [35][36][37]. Generally, an MOP can be modeled by…”

Section: Multiobjective Optimization Problemsmentioning

confidence: 99%

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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“…Proposed approach outperforms nCo approaches. This approach is further refined by using a multi-objective immune algorithm [60].…”

Section: Hybrid and Other Approachesmentioning

confidence: 99%

Recommender Systems Based on Evolutionary Computing: A Survey

Sadeghi¹,

Asghari²

2017

JSEA

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Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.

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NNIA-RS: A multi-objective optimization based recommender system

Cited by 54 publications

References 30 publications

Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization

Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Recommender Systems Based on Evolutionary Computing: A Survey

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