A Hybrid Multimodal Deep Learning Framework for Intelligent Fashion Recommendation

The rapid expansion of online fashion platforms has created an increasing demand for intelligent recommender systems capable of understanding both visual and textual cues. This paper proposes a hybrid multimodal deep learning framework for fashion recommendation that jointly addresses two key tasks: outfit compatibility prediction and complementary item retrieval. The model leverages the visual and textual encoders of the CLIP architecture to obtain joint latent representations of fashion items, which are then integrated into a unified feature vector and processed by a transformer encoder. For compatibility prediction, an "outfit token" is introduced to model the holistic relationships among items, achieving an AUC of 0.95 on the Polyvore dataset. For complementary item retrieval, a "target item token" representing the desired item description is used to retrieve compatible items, reaching an accuracy of 69.24% under the Fill-in-the-Blank (FITB) metric. The proposed approach demonstrates strong performance across both tasks, highlighting the effectiveness of multimodal learning for fashion recommendation.

Sentiment-Driven Community Detection in a Network of Perfume Preferences

Network analysis is increasingly important across various fields, including the fragrance industry, where perfumes are represented as nodes and shared user preferences as edges in perfume networks. Community detection can uncover clusters of similar perfumes, providing insights into consumer preferences, enhancing recommendation systems, and informing targeted marketing strategies. This study aims to apply community detection techniques to group perfumes favored by users into relevant clusters for better recommendations. We constructed a bipartite network from user reviews on the Persian retail platform "Atrafshan," with nodes representing users and perfumes, and edges formed by positive comments. This network was transformed into a Perfume Co-Preference Network, connecting perfumes liked by the same users. By applying community detection algorithms, we identified clusters based on shared preferences, enhancing our understanding of user sentiment in the fragrance market. To improve sentiment analysis, we integrated emojis and a user voting system for greater accuracy. Emojis, aligned with their Persian counterparts, captured the emotional tone of reviews, while user ratings for scent, longevity, and sillage refined sentiment classification. Edge weights were adjusted by combining adjacency values with user ratings in a 60:40 ratio, reflecting both connection strength and user preferences. These enhancements led to improved modularity of detected communities, resulting in more accurate perfume groupings. This research pioneers the use of community detection in perfume networks, offering new insights into consumer preferences. Our advancements in sentiment analysis and edge weight refinement provide actionable insights for optimizing product recommendations and marketing strategies in the fragrance industry.