Enhancing Image Recommendations in Social Contexts using Hierarchical Attention

Abstract

The paper introduces a novel approach to address the computational challenges faced by recommender systems operating on large-scale datasets, particularly in the context of social contextual image recommendation. Recognizing the need for a more efficient means of comparing numerous items to identify users' preferences, the proposed hierarchical consideration model delves into three crucial factors: transfer history, social impact, and owner adoration. These factors encapsulate nuanced aspects of user preferences, deriving from intricate relationships between users and images. To operationalize this, a hierarchical attention network is designed, explicitly reflecting the hierarchical nature of users' latent interests within the identified key aspects. Leveraging embeddings from state-of-the-art deep learning models tailored for different data types, the hierarchical attention network dynamically adjusts its focus on varying content levels. Extensive experimentation on real-world datasets underscores the model's superiority, with compelling results demonstrating its effectiveness and adaptability, particularly in contrast to existing approaches. The study culminates in highlighting the model's prowess in navigating diverse data landscapes, establishing its potential as an advanced solution for large-scale recommender systems.