402 research outputs found
SAIN: Self-Attentive Integration Network for Recommendation
With the growing importance of personalized recommendation, numerous
recommendation models have been proposed recently. Among them, Matrix
Factorization (MF) based models are the most widely used in the recommendation
field due to their high performance. However, MF based models suffer from cold
start problems where user-item interactions are sparse. To deal with this
problem, content based recommendation models which use the auxiliary attributes
of users and items have been proposed. Since these models use auxiliary
attributes, they are effective in cold start settings. However, most of the
proposed models are either unable to capture complex feature interactions or
not properly designed to combine user-item feedback information with content
information. In this paper, we propose Self-Attentive Integration Network
(SAIN) which is a model that effectively combines user-item feedback
information and auxiliary information for recommendation task. In SAIN, a
self-attention mechanism is used in the feature-level interaction layer to
effectively consider interactions between multiple features, while the
information integration layer adaptively combines content and feedback
information. The experimental results on two public datasets show that our
model outperforms the state-of-the-art models by 2.13%Comment: SIGIR 201
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering methods. Another thread is to transfer knowledge from other
source domains such as improving the movie recommendation with the knowledge
from the book domain, leading to transfer learning methods. In real-world life,
no single service can satisfy a user's all information needs. Thus it motivates
us to exploit both auxiliary and source information for RSs in this paper. We
propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH)
methods for cross-domain recommendation with unstructured text in an end-to-end
manner. TMH attentively extracts useful content from unstructured text via a
memory module and selectively transfers knowledge from a source domain via a
transfer network. On two real-world datasets, TMH shows better performance in
terms of three ranking metrics by comparing with various baselines. We conduct
thorough analyses to understand how the text content and transferred knowledge
help the proposed model.Comment: 11 pages, 7 figures, a full version for the WWW 2019 short pape
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
Recently, recommender systems have been able to emit substantially improved
recommendations by leveraging user-provided reviews. Existing methods typically
merge all reviews of a given user or item into a long document, and then
process user and item documents in the same manner. In practice, however, these
two sets of reviews are notably different: users' reviews reflect a variety of
items that they have bought and are hence very heterogeneous in their topics,
while an item's reviews pertain only to that single item and are thus topically
homogeneous. In this work, we develop a novel neural network model that
properly accounts for this important difference by means of asymmetric
attentive modules. The user module learns to attend to only those signals that
are relevant with respect to the target item, whereas the item module learns to
extract the most salient contents with regard to properties of the item. Our
multi-hierarchical paradigm accounts for the fact that neither are all reviews
equally useful, nor are all sentences within each review equally pertinent.
Extensive experimental results on a variety of real datasets demonstrate the
effectiveness of our method
Network On Network for Tabular Data Classification in Real-world Applications
Tabular data is the most common data format adopted by our customers ranging
from retail, finance to E-commerce, and tabular data classification plays an
essential role to their businesses. In this paper, we present Network On
Network (NON), a practical tabular data classification model based on deep
neural network to provide accurate predictions. Various deep methods have been
proposed and promising progress has been made. However, most of them use
operations like neural network and factorization machines to fuse the
embeddings of different features directly, and linearly combine the outputs of
those operations to get the final prediction. As a result, the intra-field
information and the non-linear interactions between those operations (e.g.
neural network and factorization machines) are ignored. Intra-field information
is the information that features inside each field belong to the same field.
NON is proposed to take full advantage of intra-field information and
non-linear interactions. It consists of three components: field-wise network at
the bottom to capture the intra-field information, across field network in the
middle to choose suitable operations data-drivenly, and operation fusion
network on the top to fuse outputs of the chosen operations deeply. Extensive
experiments on six real-world datasets demonstrate NON can outperform the
state-of-the-art models significantly. Furthermore, both qualitative and
quantitative study of the features in the embedding space show NON can capture
intra-field information effectively
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
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
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Recommendation models mainly deal with categorical variables, such as
user/item ID and attributes. Besides the high-cardinality issue, the
interactions among such categorical variables are usually long-tailed, with the
head made up of highly frequent values and a long tail of rare ones. This
phenomenon results in the data sparsity issue, making it essential to
regularize the models to ensure generalization. The common practice is to
employ grid search to manually tune regularization hyperparameters based on the
validation data. However, it requires non-trivial efforts and large computation
resources to search the whole candidate space; even so, it may not lead to the
optimal choice, for which different parameters should have different
regularization strengths. In this paper, we propose a hyperparameter
optimization method, LambdaOpt, which automatically and adaptively enforces
regularization during training. Specifically, it updates the regularization
coefficients based on the performance of validation data. With LambdaOpt, the
notorious tuning of regularization hyperparameters can be avoided; more
importantly, it allows fine-grained regularization (i.e. each parameter can
have an individualized regularization coefficient), leading to better
generalized models. We show how to employ LambdaOpt on matrix factorization, a
classical model that is representative of a large family of recommender models.
Extensive experiments on two public benchmarks demonstrate the superiority of
our method in boosting the performance of top-K recommendation.Comment: Accepted by KDD 201
NAIRS: A Neural Attentive Interpretable Recommendation System
In this paper, we develop a neural attentive interpretable recommendation
system, named NAIRS. A self-attention network, as a key component of the
system, is designed to assign attention weights to interacted items of a user.
This attention mechanism can distinguish the importance of the various
interacted items in contributing to a user profile. Based on the user profiles
obtained by the self-attention network, NAIRS offers personalized high-quality
recommendation. Moreover, it develops visual cues to interpret recommendations.
This demo application with the implementation of NAIRS enables users to
interact with a recommendation system, and it persistently collects training
data to improve the system. The demonstration and experimental results show the
effectiveness of NAIRS.Comment: This paper was published as a demonstration paper on WSDM'19. In this
version, we added a detailed related work sectio
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