217 research outputs found
General Partial Label Learning via Dual Bipartite Graph Autoencoder
We formulate a practical yet challenging problem: General Partial Label
Learning (GPLL). Compared to the traditional Partial Label Learning (PLL)
problem, GPLL relaxes the supervision assumption from instance-level --- a
label set partially labels an instance --- to group-level: 1) a label set
partially labels a group of instances, where the within-group instance-label
link annotations are missing, and 2) cross-group links are allowed ---
instances in a group may be partially linked to the label set from another
group. Such ambiguous group-level supervision is more practical in real-world
scenarios as additional annotation on the instance-level is no longer required,
e.g., face-naming in videos where the group consists of faces in a frame,
labeled by a name set in the corresponding caption. In this paper, we propose a
novel graph convolutional network (GCN) called Dual Bipartite Graph Autoencoder
(DB-GAE) to tackle the label ambiguity challenge of GPLL. First, we exploit the
cross-group correlations to represent the instance groups as dual bipartite
graphs: within-group and cross-group, which reciprocally complements each other
to resolve the linking ambiguities. Second, we design a GCN autoencoder to
encode and decode them, where the decodings are considered as the refined
results. It is worth noting that DB-GAE is self-supervised and transductive, as
it only uses the group-level supervision without a separate offline training
stage. Extensive experiments on two real-world datasets demonstrate that DB-GAE
significantly outperforms the best baseline over absolute 0.159 F1-score and
24.8% accuracy. We further offer analysis on various levels of label
ambiguities.Comment: 8 page
Recommendations for item set completion: On the semantics of item co-occurrence with data sparsity, input size, and input modalities
We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender performance. We also investigate data sparsity through a pruning parameter and the influence of using additional metadata. As recommender models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. This outcome means that the semantics of item co-occurrence is an important factor. The simple item co-occurrence model is a strong baseline for citation recommendation. However, autoencoders have the advantage to enable exploiting additional metadata besides the partial item set as input and achieve comparable performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the result. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
A Comprehensive Survey on Graph Summarization with Graph Neural Networks
As large-scale graphs become more widespread, more and more computational
challenges with extracting, processing, and interpreting large graph data are
being exposed. It is therefore natural to search for ways to summarize these
expansive graphs while preserving their key characteristics. In the past, most
graph summarization techniques sought to capture the most important part of a
graph statistically. However, today, the high dimensionality and complexity of
modern graph data are making deep learning techniques more popular. Hence, this
paper presents a comprehensive survey of progress in deep learning
summarization techniques that rely on graph neural networks (GNNs). Our
investigation includes a review of the current state-of-the-art approaches,
including recurrent GNNs, convolutional GNNs, graph autoencoders, and graph
attention networks. A new burgeoning line of research is also discussed where
graph reinforcement learning is being used to evaluate and improve the quality
of graph summaries. Additionally, the survey provides details of benchmark
datasets, evaluation metrics, and open-source tools that are often employed in
experimentation settings, along with a discussion on the practical uses of
graph summarization in different fields. Finally, the survey concludes with a
number of open research challenges to motivate further study in this area.Comment: 20 pages, 4 figures, 3 tables, Journal of IEEE Transactions on
Artificial Intelligenc
- …