Balancing accuracy and diversity in recommendations using matrix completion framework

Gogna, Anupriya; Majumdar, Angshul

doi:10.1016/j.knosys.2017.03.023

Cited by 32 publications

(21 citation statements)

References 41 publications

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“…To improve the user experience in RSs, researchers have attempted to improve the novelty and diversity of suggested items and to improve the visibility of long tail items [24,26,27]. Attempts to improve RS accuracy have focused on finding items highly similar to the past preferences of active users while attempts to improve diversity have focused on evaluating "dissimilarity" among recommended items.…”

Section: Improving Rs Diversitymentioning

confidence: 99%

“…Proposed models for improving RS diversity can be categorized as two-stage models and unified models. Unified models [24][25][26][27][28] have a single stage optimization framework for solving the joint formulation of a weighted combination of diversity and accuracy. For example, the DiABlO single stage optimization solution proposed in [26] was based on latent factor models.…”

Section: Improving Rs Diversitymentioning

confidence: 99%

“…A diversity enhancement regularization term was then used to improve recommendation diversity by constraining the matrix factor decomposition model. In [27], the authors presented a unified framework and a unified convex formulation based on available ratings and item metadata. The split Bregman technique used to solve this formulation achieved an acceptable accuracy-diversity trade-off.…”

Section: Improving Rs Diversitymentioning

confidence: 99%

“…Therefore, an effective RS requires not only high accuracy, but also high diversity. To enhance the diversity of a recommendation list, researchers have attempted to develop methods of maximizing the range of interests considered by the RS [27]. Since increasing diversity reduces prediction accuracy, the optimal trade-off [28,29] between diversity and accuracy must be determined.…”

Section: Introductionmentioning

confidence: 99%

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Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

et al. 2020

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Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

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Section: Improving Rs Diversitymentioning

confidence: 99%

Section: Improving Rs Diversitymentioning

confidence: 99%

Section: Improving Rs Diversitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

et al. 2020

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“…The increase of either diversity comes with a decrease in recommendation accuracy [16]. In addition, increasing ID does not necessarily lead to a significant improvement in AD, and vice versa [8,15,17]. For example, high ID can be obtained by recommending the same diverse set of products to all customers, but AD still remains low.…”

Section: Introductionmentioning

confidence: 99%

An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations

Wang¹,

Yu²,

Deng³

2018

Electron Commer Res

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The effectiveness of product recommendations is previously assessed based on recommendation accuracy. Recently, individual diversity and aggregate diversity of product recommendations have been recognized as important dimensions in evaluating the recommendation effectiveness. However, the gain of either diversity is usually at the cost of accuracy and the increase of one diversity does not guarantee a significant improvement in the other. A few attempts have been made to achieve reasonable trade-offs either between recommendation accuracy and individual diversity or between recommendation accuracy and aggregate diversity. Little attention has been paid to obtain a balance among the three important aspects of product recommendations. To address this problem, we propose an adjustable re-ranking approach that incorporates two new ranking criteria for improving both diversities. Three ranking lists are generated to guarantee recommendation accuracy, individual diversity, and aggregate diversity, respectively. The three ranking lists are finally merged with tunable parameters to generate a recommendation list. To evaluate the proposed method, experiments are conducted on a data set obtained from Alibaba. The results show that the proposed method achieves much higher improvements in both diversities than the baseline methods when sacrificing the same amount of recommendation accuracy.

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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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Balancing accuracy and diversity in recommendations using matrix completion framework

Cited by 32 publications

References 41 publications

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations

Multicriteria recommendation based on bacterial foraging optimization

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