5 research outputs found
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a
list of non-discrete attributes for each entity. Intuitively, these attributes
such as height, price or population count are able to richly characterize
entities in knowledge graphs. This additional source of information may help to
alleviate the inherent sparsity and incompleteness problem that are prevalent
in knowledge graphs. Unfortunately, many state-of-the-art relational learning
models ignore this information due to the challenging nature of dealing with
non-discrete data types in the inherently binary-natured knowledge graphs. In
this paper, we propose a novel multi-task neural network approach for both
encoding and prediction of non-discrete attribute information in a relational
setting. Specifically, we train a neural network for triplet prediction along
with a separate network for attribute value regression. Via multi-task
learning, we are able to learn representations of entities, relations and
attributes that encode information about both tasks. Moreover, such attributes
are not only central to many predictive tasks as an information source but also
as a prediction target. Therefore, models that are able to encode, incorporate
and predict such information in a relational learning context are highly
attractive as well. We show that our approach outperforms many state-of-the-art
methods for the tasks of relational triplet classification and attribute value
prediction.Comment: Accepted at CIKM 201
Efficiently Embedding Dynamic Knowledge Graphs
Knowledge graph (KG) embedding encodes the entities and relations from a KG
into low-dimensional vector spaces to support various applications such as KG
completion, question answering, and recommender systems. In real world,
knowledge graphs (KGs) are dynamic and evolve over time with addition or
deletion of triples. However, most existing models focus on embedding static
KGs while neglecting dynamics. To adapt to the changes in a KG, these models
need to be re-trained on the whole KG with a high time cost.
In this paper, to tackle the aforementioned problem, we propose a new
context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports
the embedding learning in an online fashion. DKGE introduces two different
representations (i.e., knowledge embedding and contextual element embedding)
for each entity and each relation, in the joint modeling of entities and
relations as well as their contexts, by employing two attentive graph
convolutional networks, a gate strategy, and translation operations. This
effectively helps limit the impacts of a KG update in certain regions, not in
the entire graph, so that DKGE can rapidly acquire the updated KG embedding by
a proposed online learning algorithm. Furthermore, DKGE can also learn KG
embedding from scratch. Experiments on the tasks of link prediction and
question answering in a dynamic environment demonstrate the effectiveness and
efficiency of DKGE.Comment: 14 page
Non-Parametric Estimation of Multiple Embeddings for Link Prediction on Dynamic Knowledge Graphs
Knowledge graphs play a significant role in many intelligent systems such as semantic search and recommendation systems. Recent works in this area of knowledge graph embeddings such as TransE, TransH and TransR have shown extremely competitive and promising results in relational learning. In this paper, we propose a novel extension of the translational embedding model to solve three main problems of the current models. Firstly, translational models are highly sensitive to hyperparameters such as margin and learning rate. Secondly, the translation principle only allows one spot in vector space for each golden triplet. Thus, congestion of entities and relations in vector space may reduce precision. Lastly, the current models are not able to handle dynamic data especially the introduction of new unseen entities/relations or removal of triplets. In this paper, we propose Parallel Universe TransE (puTransE), an adaptable and robust adaptation of the translational model. Our approach non-parametrically estimates the energy score of a triplet from multiple embedding spaces of structurally and semantically aware triplet selection. Our proposed approach is simple, robust and parallelizable. Our experimental results show that our proposed approach outperforms TransE and many other embedding methods for link prediction on knowledge graphs on both public benchmark dataset and a real world dynamic dataset