Multi-objective optimization for long tail recommendation

Wang, Shanfeng; Gong, Maoguo; Li, Haoliang; Yang, Jianji

doi:10.1016/j.knosys.2016.04.018

Cited by 110 publications

(55 citation statements)

References 48 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%

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%

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

Hassan¹,

Hamada²

2018

IJCIS

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“…As a result, e-commerce sites should recommend the "head" mainstream products to consumers as well as the "tail" niche products to meet the different preferences of different consumers. Long tail theory provides the theoretical foundation of this recommendation mode [19]. In addition, it has been confirmed that anchoring effect generally exists in the case of consumers facing multiple choices [20].…”

Section: Research Modelmentioning

confidence: 82%

“…Fleder and Hosanagar prove that if recommendation systems only choose to recommend products with high sales, it would lead to a higher sales concentration [13]. Thus, it is necessary to have a recommendation framework that recommends unpopular items meanwhile minimizing the accuracy loss [19].…”

Section: Long Tail Theorymentioning

confidence: 99%

Empirical Research on the Impact of Personalized Recommendation Diversity

Lin¹,

Yan²,

Lü³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Personalized recommendation has important implications in raising online shopping efficiency and increasing product sales. There has been wide interest in finding ways to provide more efficient personalized recommendations. Most existing studies focus on how to improve the accuracy of the recommendation algorithms, or are more concerned on ways to increase consumer satisfaction. Unlike these studies, our study focuses on the process of decision-making, using long tail theory as a basis, to reveal the mechanisms involved in consumers' adoption of recommendations. This paper analyzes the effect of personalized recommendations from two angles: product sales and ratings, and tries to point out differences in consumer preferences between mainstream products and niche products, high rating products and low rating products, search products and experience products. The study verifies that consumers demand diversity in the recommended content, and also provides suggestions on how to better plan and operate a personalized recommendation system.

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“…Another way to face the optimization trade-off is using a multi-objective approach, as in [27]. To optimize accuracy and the presence of long tail items, the authors propose an evolutionary algorithm that aims to find a set of solutions by optimizing two objective functions simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…The consequence is that RS that suggest long tail items have a real risk of decreasing their accuracy. Therefore, RS need to assume a trade-off between accuracy and the possibility of suggesting not-so-popular items that, however, may be useful for users that probably do not even know about their existence; see [4,11,27,29,22,1].…”

Section: Introductionmentioning

confidence: 99%

Optimizing novelty and diversity in recommendations

et al. 2018

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The articles in the long tail are those that are not popular in some sense, but all together often represent a large proportion of the products covered by a Recommender System. For companies, it is important to recommend these items that otherwise could be unknown to their customers. It is also interesting for users because knowing about these items might constitute a pleasant surprise. But long tail items are not the only we might wish to recommend. Thus, some companies promote products on seasonal offers. It is a challenge to manage the preferences on items whose interaction with users is scarce. There is a trade-off between recommending items that users like and those belonging to a certain kind. We present a framework to address recommendations where the items will have a weight that quantifies our interest in recommending them in a broad sense. Then we derive a factorization method that optimizes the award of the recommendations. To test the method, we present an exhaustive experimentation with a real-world dataset on digital news. We show that it is possible to improve dramatically the novelty This work was funded by grants TIN2015-65069-C2-1-R and TIN2015-65069-C2-2-R from Ministerio de Economía y Competitividad. We also would like to thank the newspaper El País for providing us with the dataset used in this paper.

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Multi-objective optimization for long tail recommendation

Cited by 110 publications

References 48 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

Empirical Research on the Impact of Personalized Recommendation Diversity

Optimizing novelty and diversity in recommendations

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