Aesthetic-based Clothing Recommendation (1809.05822v1)

Abstract: Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.

• The paper introduces a Dynamic Collaborative Filtering model that leverages a Brain-inspired Deep Network to extract aesthetic features for enhanced clothing recommendations.
• It combines aesthetic and CNN features using coupled matrix and tensor factorization to mitigate data sparsity while modeling user-clothing-time interactions.
• Experimental results on an Amazon dataset show significant gains in Recall and NDCG over baseline models, underscoring the impact of aesthetics on consumer choices.

Aesthetic-based Clothing Recommendation

The paper "Aesthetic-based Clothing Recommendation" addresses a significant gap in current clothing recommendation systems, which often overlook the importance of aesthetic preferences in users’ purchasing decisions. Current methods predominantly leverage features like those derived from convolutional neural networks (CNN) and the scale-invariant feature transform (SIFT), which capture semantic content but fall short in assessing whether an item is visually appealing to a consumer. The proposed approach introduces aesthetic features that are extracted using a pre-trained neural network specifically designed for aesthetic assessment.

Methodology and Contributions

The research outlines a novel method to integrate aesthetic features into clothing recommendation systems to more accurately reflect user preferences. This involves the development of a Dynamic Collaborative Filtering (DCF) model that performs tensor factorization with aesthetic features. Key elements of the methodology include:

1. Aesthetic Feature Extraction:
   • Utilizes a Brain-inspired Deep Network (BDN) trained for aesthetic assessment. This neural network draws on principles from human perception to extract meaningful, high-level aesthetic features from images, transcending the more traditional and purely semantic information gleaned from CNNs.
2. Dynamic Collaborative Filtering (DCF) Model:
   • The model acknowledges the subjective and temporally dynamic nature of aesthetic preferences. It employs tensor factorization to consider user-clothing-time interactions, thereby enabling more personalized clothing recommendations.
3. Coupled Matrix and Tensor Factorization:
   • To counter the inherent data sparsity issues in tensor factorization, the model integrates coupled matrices that link time, users, and items, reflecting a more nuanced and contextual understanding of consumer behavior.
4. Incorporation of Side Information:
   • The model extends existing tensor factorization approaches by incorporating side information — specifically, aesthetic and CNN features. This integration enhances the ability to predict preferences accurately, yielding more reliable recommendations.

Experimental Results

The authors validate their approach using a dataset from Amazon, focusing on clothing, shoes, and jewelry categories. The inclusion of aesthetic features significantly outperforms baseline models such as VBPR and other state-of-the-art recommendation techniques when evaluated using metrics like Recall and NDCG. The experimental results affirm that aesthetic preferences hold substantial sway over consumer choices, particularly in the clothing domain.

Implications and Future Directions

The incorporation of aesthetic features into clothing recommendation systems constitutes an important advancement, with practical implications for e-commerce platforms aiming to enhance consumer engagement and satisfaction by aligning recommendations more closely with users' visual tastes. The theoretical implications extend into areas like contextual and content-aware recommender systems, providing a framework that could be adapted to other domains where aesthetics play a central role.

Moving forward, the exploration of larger and more diverse datasets specific to clothing aesthetics could help refine and potentially broaden the applicability of these methods. Future research might also investigate integrating additional layers of contextual information, such as social trends and personal emotions, which further influence aesthetic appreciation. The burgeoning field of explainable AI could also benefit from such aesthetic models to offer users clearer insights into why specific items are recommended, fostering transparency and trust in automated recommendations.

In conclusion, this paper underscores the critical role of aesthetic features in recommendation systems, providing a pathway for more personalized and contextually enriched consumer experiences.