Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks

12/28/2019
by   Tieyun Qian, et al.
0

Matrix completion is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness, existing methods focus on modeling the user-item interaction graph. The inherent drawback of such methods is that their performance is bound to the density of the interactions, which is however usually of high sparsity. More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. This leads to the capability of learning embeddings for cold start users/items. Our AGNN can produce the preference embedding for a cold user/item by learning on the distribution of attributes with an extended variational auto-encoder structure. Moreover, we propose a new graph neural network variant, i.e., gated-GNN, to effectively aggregate various attributes of different modalities in a neighborhood. Empirical results on two real-world datasets demonstrate that our model yields significant improvements for cold start recommendations and outperforms or matches state-of-the-arts performance in the warm start scenario.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8

research
03/10/2021

Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

Many practical recommender systems provide item recommendation for diffe...
research
07/09/2020

Inductive Relational Matrix Completion

Data sparsity and cold-start issues emerge as two major bottlenecks for ...
research
08/14/2023

Cross-Attribute Matrix Factorization Model with Shared User Embedding

Over the past few years, deep learning has firmly established its prowes...
research
08/11/2022

Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation

The cold-start problem is a long-standing challenge in recommender syste...
research
08/16/2019

Do Co-purchases Reveal Preferences? Explainable Recommendation with Attribute Networks

With the prosperity of business intelligence, recommender systems have e...
research
05/25/2021

Graph Neural Network Based VC Investment Success Prediction

Predicting the start-ups that will eventually succeed is essentially imp...
research
11/04/2021

My House, My Rules: Learning Tidying Preferences with Graph Neural Networks

Robots that arrange household objects should do so according to the user...

Please sign up or login with your details

Forgot password? Click here to reset