Recommendation and Opinion Mining with Visual Signals

author: Julian McAuley, Department of Computer Science and Engineering, UC San Diego
published: Oct. 12, 2016,   recorded: August 2016,   views: 1302

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Building personalized systems for fashion recommendation presents several challenges due to the complicated semantics of people's preferences and styles. One challenge is simply the need to deal with sparse, long-tailed datasets, where new content is constantly introduced and recommendation is inherently a cold-start problem. Another challenge is the need to model visual signals, where the semantics of what makes items "attractive" are incredibly subtle. Finally, there is the need to model temporal dynamics that account for how fashion continually (and rapidly) evolves. In this talk we'll see how traditional recommendation approaches can be extended to explicitly account for the visual appearance of the items being recommended, in order to overcome these challenges and make visually- and stylistically-aware recommendations.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: