REVOLUTIONIZING TREND RECOMMENDATIONS: A DEEP LEARNING APPROACH FOR IMAGE-BASED INSIGHTS - R N, Ashlin Deepa; V., Srilakshmi; Ch. Vidyadhari; Nimmala, Srihithagunapriya; Rao Gundarapu, Mallikarjuna (ShodhKosh: Journal of Visual and Performing Arts, 2024)
The rapid increases in visual content creation across different sectors, starting from social media to even e-commerce, have had a major impact on their respective users, who want bespoke experiences: preferences are recognized and satisfied by recommendations contextualized within a specific sense of the world. Over many years, traditional recommendation methods took into consideration only textual content, user profiles, or purchase history as the material for recommendations. As images become the staple of engagement in the digital world, the old system misses those very nuances of visual preference that affect choice through color, texture, and style. This shift has paved way for image-based recommendation systems. Indeed, these are further dependent on powerful machine learning and deep learning models in the analysis of visual data to discern meaningful patterns in drawing recommendations in line with a user’s visual taste. In such industries as fashion and design, social media, style and aesthetic become much stronger drivers of engagement. We propose in this paper a sophisticated AI-based Trend Recommendation System, which combines deep learning model feature extraction abilities with the precision of the Nearest Neighbor Search algorithms. Our system has at its core the ResNet50 model, a widely recognized CNN which is associated with superior ability to analyze images. This is achieved using ResNet50 to extract features from the images at a deep level so that their style and trend may be characterized with intricate visual characteristics. These features are then compared to a large curated dataset using Nearest Neighbor Search, in turn ensuring that the recommended images are not only visually relevant but also contextually accurate. Our research expands ResNet50 by evaluating other state-of-the-art CNN models, including VGG16, InceptionV3, and MobileNetV2. These models are tested on their ability to extract features, provide recommendations with accuracy, and consume fewer computations. By comparing these models, we will know which one is more effective for real applications for speed and precision.This study is a landmark in developing an image-based recommender system and finds how deep learning has the transforming capability to make experience highly personalized and visually oriented.