332 research outputs found
Learning the Structure of Auto-Encoding Recommenders
Autoencoder recommenders have recently shown state-of-the-art performance in
the recommendation task due to their ability to model non-linear item
relationships effectively. However, existing autoencoder recommenders use
fully-connected neural network layers and do not employ structure learning.
This can lead to inefficient training, especially when the data is sparse as
commonly found in collaborative filtering. The aforementioned results in lower
generalization ability and reduced performance. In this paper, we introduce
structure learning for autoencoder recommenders by taking advantage of the
inherent item groups present in the collaborative filtering domain. Due to the
nature of items in general, we know that certain items are more related to each
other than to other items. Based on this, we propose a method that first learns
groups of related items and then uses this information to determine the
connectivity structure of an auto-encoding neural network. This results in a
network that is sparsely connected. This sparse structure can be viewed as a
prior that guides the network training. Empirically we demonstrate that the
proposed structure learning enables the autoencoder to converge to a local
optimum with a much smaller spectral norm and generalization error bound than
the fully-connected network. The resultant sparse network considerably
outperforms the state-of-the-art methods like \textsc{Mult-vae/Mult-dae} on
multiple benchmarked datasets even when the same number of parameters and flops
are used. It also has a better cold-start performance.Comment: Proceedings of The Web Conference 202
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
In this paper, we present a MLP-like architecture for sequential
recommendation, namely TriMLP, with a novel Triangular Mixer for cross-token
communications. In designing Triangular Mixer, we simplify the cross-token
operation in MLP as the basic matrix multiplication, and drop the
lower-triangle neurons of the weight matrix to block the anti-chronological
order connections from future tokens. Accordingly, the information leakage
issue can be remedied and the prediction capability of MLP can be fully
excavated under the standard auto-regressive mode. Take a step further, the
mixer serially alternates two delicate MLPs with triangular shape, tagged as
global and local mixing, to separately capture the long range dependencies and
local patterns on fine-grained level, i.e., long and short-term preferences.
Empirical study on 12 datasets of different scales (50K\textasciitilde 10M
user-item interactions) from 4 benchmarks (Amazon, MovieLens, Tenrec and LBSN)
show that TriMLP consistently attains promising accuracy/efficiency trade-off,
where the average performance boost against several state-of-the-art baselines
achieves up to 14.88% with 8.65% less inference cost.Comment: 15 pages, 9 figures, 5 table
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure
Graph Masked Autoencoder for Sequential Recommendation
While some powerful neural network architectures (e.g., Transformer, Graph
Neural Networks) have achieved improved performance in sequential
recommendation with high-order item dependency modeling, they may suffer from
poor representation capability in label scarcity scenarios. To address the
issue of insufficient labels, Contrastive Learning (CL) has attracted much
attention in recent methods to perform data augmentation through embedding
contrasting for self-supervision. However, due to the hand-crafted property of
their contrastive view generation strategies, existing CL-enhanced models i)
can hardly yield consistent performance on diverse sequential recommendation
tasks; ii) may not be immune to user behavior data noise. In light of this, we
propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential
Recommender system (MAERec) that adaptively and dynamically distills global
item transitional information for self-supervised augmentation. It naturally
avoids the above issue of heavy reliance on constructing high-quality embedding
contrastive views. Instead, an adaptive data reconstruction paradigm is
designed to be integrated with the long-range item dependency modeling, for
informative augmentation in sequential recommendation. Extensive experiments
demonstrate that our method significantly outperforms state-of-the-art baseline
models and can learn more accurate representations against data noise and
sparsity. Our implemented model code is available at
https://github.com/HKUDS/MAERec.Comment: This paper has been published as a full paper at SIGIR 202
On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation
In recommender systems, knowledge graph (KG) can offer critical information
that is lacking in the original user-item interaction graph (IG). Recent
process has explored this direction and shows that contrastive learning is a
promising way to integrate both. However, we observe that existing KG-enhanced
recommenders struggle in balancing between the two contrastive views of IG and
KG, making them sometimes even less effective than simply applying contrastive
learning on IG without using KG. In this paper, we propose a new contrastive
learning framework for KG-enhanced recommendation. Specifically, to make full
use of the knowledge, we construct two separate contrastive views for KG and
IG, and maximize their mutual information; to ease the contrastive learning on
the two views, we further fuse KG information into IG in a one-direction
manner.Extensive experimental results on three real-world datasets demonstrate
the effectiveness and efficiency of our method, compared to the
state-of-the-art. Our code is available through the anonymous
link:https://figshare.com/articles/conference_contribution/SimKGCL/2278338
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