What question did this study set out to answer?

To develop and evaluate a hybrid collaborative filtering model for enhancing recommendation accuracy based on historical user interactions.

March 13, 2026Discover ComputingOpen Access

Product recommendation framework with enhanced item-based collaborative filtering

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Authors

AKArchana Praveen KumarManipal Academy of Higher Education CChinmaiManipal Academy of Higher Education BKB. Priya KamathManipal Academy of Higher Education

Discussion

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Implication

This framework develops a hybrid collaborative filtering model to improve recommendation accuracy across domains, suggesting its potential in digital platforms.

Key Points

To develop and evaluate a hybrid collaborative filtering model for enhancing recommendation accuracy based on historical user interactions.
Hybrid collaborative filtering model developed
Compared with user-user collaborative filtering and neural collaborative filtering
Assessed accuracy using Food.com and MovieLens datasets
Evaluated performance using root mean square error (RMSE) metrics
Conducted bias analysis across user demographics
Hybrid model achieved RMSE of 0.5015, a 12.4% improvement over mean-centered CF
Neural Collaborative Filtering yielded lowest RMSE of 0.7138 for MovieLens
Item-item CF, SVD outperformed traditional CF with RMSEs of 0.8019 and 0.9368 respectively
Bias analysis showed consistent variations in errors across user demographics
Hybrid model more effective for sparse data, NCF better for dense contexts

Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/69b3ab2902a1e69014ccbd12 https://doi.org/10.1007/s10791-026-10030-2

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Bookmark

Implication

This framework develops a hybrid collaborative filtering model to improve recommendation accuracy across domains, suggesting its potential in digital platforms.

Product recommendation framework with enhanced item-based collaborative filtering

Discussion

Member takes

Implication

Key Points

Cite This Study

Product recommendation framework with enhanced item-based collaborative filtering

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider