25/07/2020

Multi-branch convolutional network for context-aware recommendation

Wei Guo, Can Zhang, Huifeng Guo, Ruiming Tang, Xiuqiang He

Keywords: convolutional network, context-aware recommendation

Abstract Paper Similar Papers

Abstract: Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.

The video of this talk cannot be embedded. You can watch it here:
https://dl.acm.org/doi/10.1145/3397271.3401218#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
 15:10 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 4:46 
 0
 0
 0
 0
 15:15 
 0
 0
 0
 0
 4:55 
 0
 0
 0
 0
 3:02 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 3:10 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 2:51 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 1:02 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 10:38 
 0
 0
 0
 0
 13:55 
 0
 0
 0
 0
 4:54 
 0
 0
 0
 0
 16:58 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 12:05 
 0
 0
 0
 0
 9:24 
 0
 0
 0
 0
 11:21 
 0
 0
 0
 0
 14:19 
 0
 0
 0
 0
 5:00 
 0
 0
 0
 0
 15:06 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 0:58 
 0
 0
 0
 0
 11:41 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 5:28 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 0
 0:57 
 0
 0
 0
 0
 2:57 
 0
 0
 0
 0
 17:30 
 0
 0
 0
 0
 3:13