33,034 research outputs found
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Identifying mix-and-match relationships between fashion items is an urgent
task in a fashion e-commerce recommender system. It will significantly enhance
user experience and satisfaction. However, due to the challenges of inferring
the rich yet complicated set of compatibility patterns in a large e-commerce
corpus of fashion items, this task is still underexplored. Inspired by the
recent advances in multi-relational knowledge representation learning and deep
neural networks, this paper proposes a novel Translation-based Neural Fashion
Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion
item embeddings and category-specific complementary relations in a unified
space via an end-to-end learning manner. TransNFCM places items in a unified
embedding space where a category-specific relation (category-comp-category) is
modeled as a vector translation operating on the embeddings of compatible items
from the corresponding categories. By this way, we not only capture the
specific notion of compatibility conditioned on a specific pair of
complementary categories, but also preserve the global notion of compatibility.
We also design a deep fashion item encoder which exploits the complementary
characteristic of visual and textual features to represent the fashion
products. To the best of our knowledge, this is the first work that uses
category-specific complementary relations to model the category-aware
compatibility between items in a translation-based embedding space. Extensive
experiments demonstrate the effectiveness of TransNFCM over the
state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the
relation of collaborative similarity. Nevertheless, there exist multiple
relations between items in real-world scenarios. Distinct from the
collaborative similarity that implies co-interact patterns from the user
perspective, these relations reveal fine-grained knowledge on items from
different perspectives of meta-data, functionality, etc. However, how to
incorporate multiple item relations is less explored in recommendation
research. In this work, we propose Relational Collaborative Filtering (RCF), a
general framework to exploit multiple relations between items in recommender
system. We find that both the relation type and the relation value are crucial
in inferring user preference. To this end, we develop a two-level hierarchical
attention mechanism to model user preference. The first-level attention
discriminates which types of relations are more important, and the second-level
attention considers the specific relation values to estimate the contribution
of a historical item in recommending the target item. To make the item
embeddings be reflective of the relational structure between items, we further
formulate a task to preserve the item relations, and jointly train it with the
recommendation task of preference modeling. Empirical results on two real
datasets demonstrate the strong performance of RCF. Furthermore, we also
conduct qualitative analyses to show the benefits of explanations brought by
the modeling of multiple item relations
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
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is
compulsory to go beyond modeling user-item interactions and take side
information into account. Traditional methods like factorization machine (FM)
cast it as a supervised learning problem, which assumes each interaction as an
independent instance with side information encoded. Due to the overlook of the
relations among instances or items (e.g., the director of a movie is also an
actor of another movie), these methods are insufficient to distill the
collaborative signal from the collective behaviors of users. In this work, we
investigate the utility of knowledge graph (KG), which breaks down the
independent interaction assumption by linking items with their attributes. We
argue that in such a hybrid structure of KG and user-item graph, high-order
relations --- which connect two items with one or multiple linked attributes
--- are an essential factor for successful recommendation. We propose a new
method named Knowledge Graph Attention Network (KGAT) which explicitly models
the high-order connectivities in KG in an end-to-end fashion. It recursively
propagates the embeddings from a node's neighbors (which can be users, items,
or attributes) to refine the node's embedding, and employs an attention
mechanism to discriminate the importance of the neighbors. Our KGAT is
conceptually advantageous to existing KG-based recommendation methods, which
either exploit high-order relations by extracting paths or implicitly modeling
them with regularization. Empirical results on three public benchmarks show
that KGAT significantly outperforms state-of-the-art methods like Neural FM and
RippleNet. Further studies verify the efficacy of embedding propagation for
high-order relation modeling and the interpretability benefits brought by the
attention mechanism.Comment: KDD 2019 research trac
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
Learning over Knowledge-Base Embeddings for Recommendation
State-of-the-art recommendation algorithms -- especially the collaborative
filtering (CF) based approaches with shallow or deep models -- usually work
with various unstructured information sources for recommendation, such as
textual reviews, visual images, and various implicit or explicit feedbacks.
Though structured knowledge bases were considered in content-based approaches,
they have been largely neglected recently due to the availability of vast
amount of data, and the learning power of many complex models.
However, structured knowledge bases exhibit unique advantages in personalized
recommendation systems. When the explicit knowledge about users and items is
considered for recommendation, the system could provide highly customized
recommendations based on users' historical behaviors. A great challenge for
using knowledge bases for recommendation is how to integrated large-scale
structured and unstructured data, while taking advantage of collaborative
filtering for highly accurate performance. Recent achievements on knowledge
base embedding sheds light on this problem, which makes it possible to learn
user and item representations while preserving the structure of their
relationship with external knowledge. In this work, we propose to reason over
knowledge base embeddings for personalized recommendation. Specifically, we
propose a knowledge base representation learning approach to embed
heterogeneous entities for recommendation. Experimental results on real-world
dataset verified the superior performance of our approach compared with
state-of-the-art baselines
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