Variational Bandwidth Auto-encoder for Hybrid Recommender Systems

05/17/2021
by   Yaochen Zhu, et al.
0

Hybrid recommendations have recently attracted a lot of attention where user features are utilized as auxiliary information to address the sparsity problem caused by insufficient user-item interactions. However, extracted user features generally contain rich multimodal information, and most of them are irrelevant to the recommendation purpose. Therefore, excessive reliance on these features will make the model overfit on noise and difficult to generalize. In this article, we propose a variational bandwidth auto-encoder (VBAE) for recommendations, aiming to address the sparsity and noise problems simultaneously. VBAE first encodes user collaborative and feature information into Gaussian latent variables via deep neural networks to capture non-linear user similarities. Moreover, by considering the fusion of collaborative and feature variables as a virtual communication channel from an information-theoretic perspective, we introduce a user-dependent channel to dynamically control the information allowed to be accessed from the feature embeddings. A quantum-inspired uncertainty measurement of the hidden rating embeddings is proposed accordingly to infer the channel bandwidth by disentangling the uncertainty information in the ratings from the semantic information. Through this mechanism, VBAE incorporates adequate auxiliary information from user features if collaborative information is insufficient, while avoiding excessive reliance on noisy user features to improve its generalization ability to new users. Extensive experiments conducted on three real-world datasets demonstrate the effectiveness of the proposed method. Codes and datasets are released at https://github.com/yaochenzhu/vbae.

READ FULL TEXT

page 1

page 3

page 4

page 5

page 6

page 7

page 8

page 9

research
11/21/2022

Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems

Recently, user-oriented auto-encoders (UAEs) have been widely used in re...
research
04/20/2022

Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation

Recommending appropriate tags to items can facilitate content organizati...
research
06/05/2020

Variational Auto-encoder for Recommender Systems with Exploration-Exploitation

Variational auto-encoder (VAE) is an efficient non-linear latent factor ...
research
01/06/2022

Deep Causal Reasoning for Recommendations

Traditional recommender systems aim to estimate a user's rating to an it...
research
10/11/2022

FusionDeepMF: A Dual Embedding based Deep Fusion Model for Recommendation

Traditional Collaborative Filtering (CF) based methods are applied to un...
research
02/12/2023

Denoising and Prompt-Tuning for Multi-Behavior Recommendation

In practical recommendation scenarios, users often interact with items u...
research
05/26/2017

Learning Robust Features with Incremental Auto-Encoders

Automatically learning features, especially robust features, has attract...

Please sign up or login with your details

Forgot password? Click here to reset