15,707 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
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile
method to perform machine learning on relational data such as knowledge graphs.
In this work, we propose holographic embeddings (HolE) to learn compositional
vector space representations of entire knowledge graphs. The proposed method is
related to holographic models of associative memory in that it employs circular
correlation to create compositional representations. By using correlation as
the compositional operator HolE can capture rich interactions but
simultaneously remains efficient to compute, easy to train, and scalable to
very large datasets. In extensive experiments we show that holographic
embeddings are able to outperform state-of-the-art methods for link prediction
in knowledge graphs and relational learning benchmark datasets.Comment: To appear in AAAI-1
Convolutional 2D Knowledge Graph Embeddings
Link prediction for knowledge graphs is the task of predicting missing
relationships between entities. Previous work on link prediction has focused on
shallow, fast models which can scale to large knowledge graphs. However, these
models learn less expressive features than deep, multi-layer models -- which
potentially limits performance. In this work, we introduce ConvE, a multi-layer
convolutional network model for link prediction, and report state-of-the-art
results for several established datasets. We also show that the model is highly
parameter efficient, yielding the same performance as DistMult and R-GCN with
8x and 17x fewer parameters. Analysis of our model suggests that it is
particularly effective at modelling nodes with high indegree -- which are
common in highly-connected, complex knowledge graphs such as Freebase and
YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer
from test set leakage, due to inverse relations from the training set being
present in the test set -- however, the extent of this issue has so far not
been quantified. We find this problem to be severe: a simple rule-based model
can achieve state-of-the-art results on both WN18 and FB15k. To ensure that
models are evaluated on datasets where simply exploiting inverse relations
cannot yield competitive results, we investigate and validate several commonly
used datasets -- deriving robust variants where necessary. We then perform
experiments on these robust datasets for our own and several previously
proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal
Rank across most datasets.Comment: Extended AAAI2018 pape
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