Unlocking the Potential of Context: A Contextual Neural Collaborative Filtering Framework for Rating Prediction
Rajesh Garapati, Manomita Chakraborty · Computational Intelligence · 2025
ABSTRACT The exponential growth of online multimedia content across various platforms has created an urgent need for robust assistive technologies to manage the overwhelming volume of information. Consequently, considerable efforts have been dedicated to developing sophisticated multimedia recommendation systems, with Neural Collaborative Filtering (NCF) emerging as a prevalent methodology. However, the conventional NCF method exhibits significant limitations, particularly in integrating contextual data and effectively handling sparse and imbalanced datasets. To address these limitations, this research introduces the Contextual Neural Collaborative Filtering (C‐NCF) method, which enhances the NCF framework by incorporating contextual data to enrich the learning process of user‐item interactions. The primary objective of this method is to improve rating prediction accuracy, a crucial factor in generating more effective recommendations. A key innovation of the C‐NCF method lies in its interaction mechanism, where user ratings of items are evaluated under diverse contextual conditions, assigning varying importance to each contextual factor. Extensive testing on three real‐world datasets demonstrated that the C‐NCF method outperforms existing advanced techniques. Empirical findings demonstrate that the C‐NCF method achieved an average error reduction of 36.43% in Mean Absolute Error and 36.60% in Root Mean Squared Error compared to traditional collaborative filtering, matrix factorization, and context‐aware models, significantly enhancing recommendation quality. These insights open promising avenues for further exploration in the field of context‐aware recommender systems.