20 research outputs found
Relation Embedding with Dihedral Group in Knowledge Graph
Link prediction is critical for the application of incomplete knowledge graph
(KG) in the downstream tasks. As a family of effective approaches for link
predictions, embedding methods try to learn low-rank representations for both
entities and relations such that the bilinear form defined therein is a
well-behaved scoring function. Despite of their successful performances,
existing bilinear forms overlook the modeling of relation compositions,
resulting in lacks of interpretability for reasoning on KG. To fulfill this
gap, we propose a new model called DihEdral, named after dihedral symmetry
group. This new model learns knowledge graph embeddings that can capture
relation compositions by nature. Furthermore, our approach models the relation
embeddings parametrized by discrete values, thereby decrease the solution space
drastically. Our experiments show that DihEdral is able to capture all desired
properties such as (skew-) symmetry, inversion and (non-) Abelian composition,
and outperforms existing bilinear form based approach and is comparable to or
better than deep learning models such as ConvE.Comment: ACL 201
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
Learning Feature Interactions with Lorentzian Factorization Machine
Learning representations for feature interactions to model user behaviors is
critical for recommendation system and click-trough rate (CTR) predictions.
Recent advances in this area are empowered by deep learning methods which could
learn sophisticated feature interactions and achieve the state-of-the-art
result in an end-to-end manner. These approaches require large number of
training parameters integrated with the low-level representations, and thus are
memory and computational inefficient. In this paper, we propose a new model
named "LorentzFM" that can learn feature interactions embedded in a hyperbolic
space in which the violation of triangle inequality for Lorentz distances is
available. To this end, the learned representation is benefited by the peculiar
geometric properties of hyperbolic triangles, and result in a significant
reduction in the number of parameters (20\% to 80\%) because all the top deep
learning layers are not required. With such a lightweight architecture,
LorentzFM achieves comparable and even materially better results than the deep
learning methods such as DeepFM, xDeepFM and Deep \& Cross in both
recommendation and CTR prediction tasks.Comment: 8 pages, 5 figures, accepted to AAAI-202