Post on 24-Aug-2020
Explainable Recommendation Through Attentive Multi-View Learning
Advisor: Jia-Ling Koh
Presenter: You-Xiang Chen
Source: AAAI ‘19
Data: 2020/03/02
Content
Introduction
Method
Experiment
01
Conclusion
02
03
04
Introduction
IntroductionRecommendation System
Introduction
user feature × user latent
item latent × item feature
Matrix Factorization
Introduction
Deep but unexplainable
Neural Collaborative Filtering
Introduction
We propose a Deep Explicit Attentive Multi-View Learning
Model (DEAML) for explainable recommendation:
1. improves accuracy from noisy and sparse data
2. formulates personalized explanation generation as a
constrained tree node selection problem
Problem Definition
• User set 𝑈
• Item set 𝐼
• Explicit feature hierarchy Υ
• Set the node in Υ as
ℱ = {ℱ1, … , ℱ𝐿}
• Input
• Output
• Predicted rating Ƹ𝑟𝑖𝑗
• Feature-level explanation 𝐸 (𝑠𝑢𝑏𝑠𝑒𝑡 𝑜𝑓 𝐹)
Microsoft
Concept Graph
e.g. Pork
Microsoft Concept Graph
https://concept.research.microsoft.com/
• New York (is-a) state• Name (is-a) information• Facebook (is-a) social medium
• 5 million concepts
• 85 million “IsA” relations
Relate work
• Explicit Factor Models
Enrich user & item representation by adding
set of latent factors learned from explicit feature.
capture both explicit & implicit factor
http://yongfeng.me/attach/efm-zhang.pdf
Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis
explicit factorexplicit factor
Relate work
• User/Item-feature attention matrix 𝑿, 𝒀
ℱ = ℱ1, … , ℱ𝑝 , set of explicit feature in review
• Integrating Explicit and Implicit Features𝑉𝑇: projection matrix
Explicit Factor Models for Explainable Recommendation based on Phrase-level Sentiment Analysis
Factorization Model over matrix 𝑿,𝒀
Factorization Model over matrix A
X, Y are in the range of [𝟏, 𝐍]
Method
FrameworkDeep Explicit Attentive Multi-View Learning Model
Hierarchical propagation
• Personalized User AttentionAttn. score
𝒙𝒊𝒍 measures how much user 𝒊 cares about feature 𝑭𝒍
Attentive Multi-View Learning
h=1
• Latent factors learning from explicit features
concatenation
Latent factor learn from explicit feature(EFM model)
Latent factor learn from implicit feature
(EFM model)
item representation at view h
user representation at view h
rating prediction in h view
Attentive Multi-View Learning
• Loss of each view
projection matrix
rating prediction for each view
estimating hidden representation of user/item
• Co-regularization loss
enforcing agreement
Attentive Multi-View Learning
• Weighted sum prediction in each view
Calculate attention weight
Objective function
• Jointly learning
loss of each view
Co-regularization lossWeighted sum prediction
Personalized Explanation Generation
• Utility function
user interest at level h item interest at level h
weight of view h
4
5
-1
6
2
Personalized Explanation Generation
• Constrained tree node selection
max. utility of s-th childmax. utility (s-1)-th node to t’
Experiment
Dataset
Dataset User# Item# Reviews#
Toys&Games 19,412 11,924 167,597
Digital Music 5,541 3,568 64,706
Yelp 8,744 14,082 212,922
• Statistics of the evaluation datasets
5-core
5-core
10-core
Baselines• Observed rating matrix
• NMF
• PMF
• SVD++
• Knowledge-based method
• Reviews-based method
• HFT
• EFM
• DeepCoNN
• NARRE
• CKE
Single layer structure
Deep learning base
RMSE comparison
same weight to all views
Effect of number of latent factors
Case Study
Conclusion
1. We build an initial network based on an explainable deep hierarchy
(Microsoft Concept Graph) and improve the model accuracy by optimizing
key variables in the hierarchy
2. We propose a Deep Explicit Attentive Multi-View Learning Model
(DEAML) for explainable recommendation, which combines the
advantages of deep learning-based methods and existing explainable
methods.
3. Experimental results show that our model performs better than state-of-the-
art methods in both accuracy and explainability.