Sanchez-Riera, JordiJordiSanchez-RieraLin, Jun MingJun MingLinHua, Kai LungKai LungHuaWEN-HUANG CHENGTsui, Arvin WenArvin WenTsui2023-03-082023-03-082017-01-01978331951810703029743https://scholars.lib.ntu.edu.tw/handle/123456789/628992Searching the Web has become an everyday task for most people. However, the presence of too much information can cause information overload. For example, when shopping online, a user can easily be overwhelmed by too many choices. To this end, we propose a personalized clothing recommendation system, namely i-Stylist, through the analysis of personal images in social networks. To access the available personal images of a user, the i-Stylist system extracts a number of characteristics from each clothing item such as CNN feature vectors and metadata such as color, material and pattern of the fabric. Then, these clothing items are organized as a fully connected graph to later infer the personalized probability distribution of how the user will like each clothing item in a shopping website. The user is able to modify the graph structure, e.g. adding and deleting vertices by giving feedback about the retrieved clothing items. The i-Stylist system is compared against two other baselines and demonstrated to have better performance.Clothing items | Convolutional neural networks (CNN) | Dress style | Personalized recommendation | Social networkconference paper10.1007/978-3-319-51811-4_542-s2.0-85009747694https://api.elsevier.com/content/abstract/scopus_id/85009747694