25/07/2020

Content-aware neural hashing for cold-start recommendation

Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma

Keywords: content-aware recommendation, autoencoders, collaborative filtering, hashing, cold-start recommendation

Abstract Paper Similar Papers

Abstract: Content-aware recommendation approaches are essential for providing meaningful recommendations for new (i.e.,cold-start) items in a recommender system. We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which generates binary hash codes for users and items, such that the highly efficient Hamming distance can be used for estimating user-item relevance. NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes. Inspired from semantic hashing, the item hashing component generates a hash code directly from an item’s content information (i.e., it generates cold-start and seen item hash codes in the same manner). This contrasts existing state-of-the-art models, which treat the two item cases separately. The user hash codes are generated directly based on user id, through learning a user embedding matrix. We show experimentally that NeuHash-CF significantly outperforms state-of-the-art baselines by up to 12

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3397271.3401060#sec-supp
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at SIGIR 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 8:59 
 0
 0
 0
 0
 9:19 
 0
 0
 0
 0
 9:49 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 10:14 
 0
 0
 0
 0
 17:21 
 0
 0
 0
 0
 10:07 
 0
 0
 0
 0
 19:21 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 13:52 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 2:51 
 0
 0
 0
 0
 3:22 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 9:19 
 0
 0
 0
 0
 9:57 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 6:14 
 0
 0
 0
 0
 12:51 
 0
 0
 0
 0
 5:16 
 0
 0
 0
 0
 11:55 
 0
 0
 0
 0
 12:51 
 0
 0
 0
 0
 9:18 
 0
 0
 0
 0
 10:59 
 0
 0
 0
 0
 6:09 
 0
 0
 0
 0
 16:26 
 0
 0
 0
 0
 4:56 
 0
 0
 0
 0
 12:02 
 0
 0
 0
 0
 21:30 
 0
 0
 0
 0
 12:40 
 0
 0
 0
 0
 17:56 
 0
 0
 0
 0
 11:15 
 1
 1
 1
 1
 10:57 
 0
 0
 0
 0
 16:51 
 0
 0
 0
 0
 13:20 
 0
 0
 0
 0
 14:13 
 0
 0
 0
 0
 12:59 
 0
 0
 0
 0
 11:12 
 0
 0
 0
 0
 15:46