88 research outputs found
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.Comment: WSDM 201
NRPA: Neural Recommendation with Personalized Attention
Existing review-based recommendation methods usually use the same model to
learn the representations of all users/items from reviews posted by users
towards items. However, different users have different preference and different
items have different characteristics. Thus, the same word or similar reviews
may have different informativeness for different users and items. In this paper
we propose a neural recommendation approach with personalized attention to
learn personalized representations of users and items from reviews. We use a
review encoder to learn representations of reviews from words, and a user/item
encoder to learn representations of users or items from reviews. We propose a
personalized attention model, and apply it to both review and user/item
encoders to select different important words and reviews for different
users/items. Experiments on five datasets validate our approach can effectively
improve the performance of neural recommendation.Comment: 4 pages, 4 figure
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
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Learning sophisticated feature interactions behind user behaviors is critical
in maximizing CTR for recommender systems. Despite great progress, existing
methods seem to have a strong bias towards low- or high-order interactions, or
require expertise feature engineering. In this paper, we show that it is
possible to derive an end-to-end learning model that emphasizes both low- and
high-order feature interactions. The proposed model, DeepFM, combines the power
of factorization machines for recommendation and deep learning for feature
learning in a new neural network architecture. Compared to the latest Wide \&
Deep model from Google, DeepFM has a shared input to its "wide" and "deep"
parts, with no need of feature engineering besides raw features. Comprehensive
experiments are conducted to demonstrate the effectiveness and efficiency of
DeepFM over the existing models for CTR prediction, on both benchmark data and
commercial data
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
A user can be represented as what he/she does along the history. A common way
to deal with the user modeling problem is to manually extract all kinds of
aggregated features over the heterogeneous behaviors, which may fail to fully
represent the data itself due to limited human instinct. Recent works usually
use RNN-based methods to give an overall embedding of a behavior sequence,
which then could be exploited by the downstream applications. However, this can
only preserve very limited information, or aggregated memories of a person.
When a downstream application requires to facilitate the modeled user features,
it may lose the integrity of the specific highly correlated behavior of the
user, and introduce noises derived from unrelated behaviors. This paper
proposes an attention based user behavior modeling framework called ATRank,
which we mainly use for recommendation tasks. Heterogeneous user behaviors are
considered in our model that we project all types of behaviors into multiple
latent semantic spaces, where influence can be made among the behaviors via
self-attention. Downstream applications then can use the user behavior vectors
via vanilla attention. Experiments show that ATRank can achieve better
performance and faster training process. We further explore ATRank to use one
unified model to predict different types of user behaviors at the same time,
showing a comparable performance with the highly optimized individual models.Comment: AAAI 201
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