6,142 research outputs found
Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network
In the past decade, the heterogeneous information network (HIN) has become an
important methodology for modern recommender systems. To fully leverage its
power, manually designed network templates, i.e., meta-structures, are
introduced to filter out semantic-aware information. The hand-crafted
meta-structure rely on intense expert knowledge, which is both laborious and
data-dependent. On the other hand, the number of meta-structures grows
exponentially with its size and the number of node types, which prohibits
brute-force search. To address these challenges, we propose Genetic
Meta-Structure Search (GEMS) to automatically optimize meta-structure designs
for recommendation on HINs. Specifically, GEMS adopts a parallel genetic
algorithm to search meaningful meta-structures for recommendation, and designs
dedicated rules and a meta-structure predictor to efficiently explore the
search space. Finally, we propose an attention based multi-view graph
convolutional network module to dynamically fuse information from different
meta-structures. Extensive experiments on three real-world datasets suggest the
effectiveness of GEMS, which consistently outperforms all baseline methods in
HIN recommendation. Compared with simplified GEMS which utilizes hand-crafted
meta-paths, GEMS achieves over performance gain on most evaluation
metrics. More importantly, we conduct an in-depth analysis on the identified
meta-structures, which sheds light on the HIN based recommender system design.Comment: Published in Proceedings of the 29th ACM International Conference on
Information and Knowledge Management (CIKM '20
Hybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework
Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks
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