Abstract
Searching 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.
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Notes
- 1.
Usually a popular item is defined by the number of likes, links, etc., that an image or user can have.
- 2.
- 3.
Different from the authors of this paper.
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Acknowledgement
This work is funded by ITRI Grant #105-W100-21A1, under the project “Big Data Technologies and Applications (2/4)” of the Industrial Technology Research Institute of Taiwan, R.O.C.
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Sanchez-Riera, J., Lin, JM., Hua, KL., Cheng, WH., Tsui, A.W. (2017). i-Stylist: Finding the Right Dress Through Your Social Networks. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_54
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