Bigger data set, better personalized recommendation performance?

Yang, Donghui; Yu, Guang

doi:10.1109/icmse.2013.6586258

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Such a rapid increase in data helps to improve the performance of the recommender system, but on the other hand, it also decreases the performance of the recommender system due to increased noise (Kim et al , 2012). Therefore, in order to reduce computing cost and provide effective recommendation service, a strategy to improve the performance of the recommendation algorithm by filtering only influential and meaningful data is required along with research to develop a new recommendation algorithm to increase the recommendation performance (Dong-Hui and Guang 2013). However, there have been few studies on how changes in input data affect recommendation system performance in the recommendation system research so far.…”

Section: Introductionmentioning

confidence: 99%

Impact on recommendation performance of online review helpfulness and consistency

Park

et al. 2022

DTA

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PurposeThe existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.Design/methodology/approachSome studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.FindingsThis study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.Originality/valueThis study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

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Section: Introductionmentioning

confidence: 99%