Enhancing Collaborative Filtering Recommendation by User Interest Probability

Yu, Jing; Shi, Jingjing; Chen, Yunwen; Liu, Wenhai; Li, Kai; Xie, Zhijun

doi:10.1109/icaibd51990.2021.9459028

Cited by 4 publications

(2 citation statements)

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“…Simultaneously, content-based recommendation systems have drawn interest due to their capacity to take user prefer-ences and item features into account. These technologies provide a more individualized approach by generating suggestions based on item features and user profiles [3]. To ensure that consumers are not limited to a small selection of options, there are still difficulties in guaranteeing the diversity and freshness of recommendations.…”

Section: Elated Workmentioning

confidence: 99%

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Personalized product ranking system for enhanced user experience

Santhosh,

Ashweej,

Agarwal

et al. 2024

MATEC Web Conf.

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Businesses looking to engage and satisfy their online audience must prioritize the user experience in the quickly changing digital landscape. In order to address this challenge, this project proposes and implements a sophisticated algorithmic solution designed to produce extremely accurate and user-centric product rankings. This represents a groundbreaking approach. The system attempts to predict and present the most relevant product suggestions by meticulously considering a wide range of factors such as user preferences, historical interactions, product popularity trends, and user similarity metrics. Our methodology is distinguished by the use of a dynamic simulation environment in which user profiles, product categories, and interaction pat- terns are manipulated to replicate authentic real-world scenarios. The dynamic framework facilitates the comprehensive testing and optimization of customized ranking algorithms, guaranteeing their flexibility in response to changing user preferences and behaviors. The project’s effectiveness is evaluated using precise evaluation metrics that offer quantitative information about how well the system understands and responds to each unique user’s preference, ultimately resulting in a more fulfilling and rich online shopping or content consumption experience.

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Section: Elated Workmentioning

confidence: 99%

“…Our approach centers on the development of a sophisti-cated algorithmic system to tackle the challenging problem of satisfying consumer expectations in the ever-changing online environment [3]. This algorithm takes into account a wide range of parameters in order to anticipate and display the most suitable product selections.…”

Section: Introductionmentioning

confidence: 99%