What question did this study set out to answer?

The aim is to enhance recommendation accuracy on e-commerce platforms by integrating content-based and collaborative filtering approaches.

March 30, 2026Discover Artificial IntelligenceOpen Access

An enhanced hybrid recommender system using an adaptive optimization approach

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The aim is to enhance recommendation accuracy on e-commerce platforms by integrating content-based and collaborative filtering approaches.
Developed a hybrid recommender system combining content-based filtering and collaborative filtering.
Implemented data preprocessing techniques including data cleaning and principal component analysis to reduce dimensionality.
Utilized adaptive optimization strategies for improving recommendation ratings.
Achieved minimal mean absolute percentage error (MAPE) of 0.0134 and 0.027186.
Obtained high accuracy rates of 0.9757 and 0.9879, indicating superior performance of the proposed system.
Demonstrated that the hybrid system significantly outperformed comparative algorithms like RDO, DLO, BOA, and GFO.

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Implication

This research demonstrates improved recommendation accuracy in e-commerce using a hybrid system combining content-based and collaborative filtering with adaptive optimization.

Cite This Study

Rajpoot et al. (2026) studied this question.

synapsesocial.com/papers/69c9c51bf8fdd13afe0bd06d https://doi.org/10.1007/s44163-026-00999-6

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Implication

This research demonstrates improved recommendation accuracy in e-commerce using a hybrid system combining content-based and collaborative filtering with adaptive optimization.

An enhanced hybrid recommender system using an adaptive optimization approach

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Discussion

Implication

Cite This Study

An enhanced hybrid recommender system using an adaptive optimization approach

Key Points

Discussion

Implication

Cite This Study

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