Context-Aware Deep Neural Network-Based Recommendation System

Abstract

With the exponential proliferation of digital content, users face an overwhelming paradox of choice, underscoring the critical need for advanced recommendation systems (RSs). Traditional RS approaches, however, encounter fundamental limitations in addressing implicit feedback disambiguation, contextual integration and temporal dynamics adaptation. This paper introduces a transformative neural collaborative filtering (CF) framework that addresses these challenges through three key innovations. First, we develop a novel triple-space embedding architecture that distinctly models positive, negative, and unknown interaction states, enabling our system to differentiate between genuine disinterest and simple unawareness of content. Second, we implement a multi-dimensional context integration mechanism with hierarchical temporal modeling across five distinct granularities, capturing complex cyclical patterns that existing approaches overlook. Third, we propose an adaptive preference evolution system utilizing sophisticated memory networks and hierarchical RNN architecture that effectively balances preservation of established preferences with adaptation to emerging interests. We unify these components through a joint optimization approach that simultaneously addresses prediction accuracy, temporal coherence, and contextual relevance. Comprehensive evaluation using MovieLens (1M and 20M) and Netflix Prize datasets demonstrates that our framework consistently outperforms state-of-the-art methods across various metrics, with particularly significant improvements in scenarios characterized by sparse feedback and dynamic contexts. Our approach achieves average improvements of 18.7% in Hit@10 and 22.3% in NDCG@10 (Normalized Discounted Cumulative Gain) compared to leading baselines, establishing a new benchmark for developing contextually aware and temporally adaptive RSs.