| dc.description.abstract | Clothing recommendation has always been a popular topic, especially for clothing personalization. Generally, clothing recommendation requires images or texts information, and comments or ratings as well. Our study focuses on the chromatics features on images, which is seldom discussed by previous studies. We aim to build an online personalized clothing recommendation system based on users’ aesthetic preference.
Colors were regarded as just simple features of images by previous studies, which lacked a user’s psychological factors affected by images’ chromatics. Therefore, our study discusses the aesthetic concepts that images’ chromatics brings to users and build a personalized aesthetic recommendation with higher interpretability.
Users tend to be attracted by elements in images, so we thus extract the overall, clothing, and single-color style from OB Design’s clothing dataset to build users’ aesthetic preference. There are three recommendation systems, which include content- based, model-based collaborative filtering, and hybrid methods. To elaborate, content- based method is for computation on products similarity based on chromatics, model- based collaborative filtering is to cluster user’s aesthetic preference, and hybrid method uses both methods.
We conduct three experiments and assess the results by Hit Ratio, and we find the third system using both methods perform the best among all systems. In conclusion, our methods prove that applying chromatics and aesthetic features to model users’ preference and recommend clothing items is feasible and reasonable. | en_US |