16 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
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
User Diverse Preference Modeling by Multimodal Attentive Metric Learning
Most existing recommender systems represent a user's preference with a
feature vector, which is assumed to be fixed when predicting this user's
preferences for different items. However, the same vector cannot accurately
capture a user's varying preferences on all items, especially when considering
the diverse characteristics of various items. To tackle this problem, in this
paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to
model user diverse preferences for various items. In particular, for each
user-item pair, we propose an attention neural network, which exploits the
item's multimodal features to estimate the user's special attention to
different aspects of this item. The obtained attention is then integrated into
a metric-based learning method to predict the user preference on this item. The
advantage of metric learning is that it can naturally overcome the problem of
dot product similarity, which is adopted by matrix factorization (MF) based
recommendation models but does not satisfy the triangle inequality property. In
addition, it is worth mentioning that the attention mechanism cannot only help
model user's diverse preferences towards different items, but also overcome the
geometrically restrictive problem caused by collaborative metric learning.
Extensive experiments on large-scale real-world datasets show that our model
can substantially outperform the state-of-the-art baselines, demonstrating the
potential of modeling user diverse preference for recommendation.Comment: Accepted by ACM Multimedia 2019 as a full pape