16,337 research outputs found
Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many
recommender systems. Conventional CF-based methods use the ratings given to
items by users as the sole source of information for learning to make
recommendation. However, the ratings are often very sparse in many
applications, causing CF-based methods to degrade significantly in their
recommendation performance. To address this sparsity problem, auxiliary
information such as item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking this approach which
tightly couples the two components that learn from two different sources of
information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse. To address this
problem, we generalize recent advances in deep learning from i.i.d. input to
non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian
model called collaborative deep learning (CDL), which jointly performs deep
representation learning for the content information and collaborative filtering
for the ratings (feedback) matrix. Extensive experiments on three real-world
datasets from different domains show that CDL can significantly advance the
state of the art
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure
Learning Tree-based Deep Model for Recommender Systems
Model-based methods for recommender systems have been studied extensively in
recent years. In systems with large corpus, however, the calculation cost for
the learnt model to predict all user-item preferences is tremendous, which
makes full corpus retrieval extremely difficult. To overcome the calculation
barriers, models such as matrix factorization resort to inner product form
(i.e., model user-item preference as the inner product of user, item latent
factors) and indexes to facilitate efficient approximate k-nearest neighbor
searches. However, it still remains challenging to incorporate more expressive
interaction forms between user and item features, e.g., interactions through
deep neural networks, because of the calculation cost.
In this paper, we focus on the problem of introducing arbitrary advanced
models to recommender systems with large corpus. We propose a novel tree-based
method which can provide logarithmic complexity w.r.t. corpus size even with
more expressive models such as deep neural networks. Our main idea is to
predict user interests from coarse to fine by traversing tree nodes in a
top-down fashion and making decisions for each user-node pair. We also show
that the tree structure can be jointly learnt towards better compatibility with
users' interest distribution and hence facilitate both training and prediction.
Experimental evaluations with two large-scale real-world datasets show that the
proposed method significantly outperforms traditional methods. Online A/B test
results in Taobao display advertising platform also demonstrate the
effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201
Deep Learning for Recommender Systems
The widespread adoption of the Internet has led to an explosion in the number of choices available to consumers. Users begin to expect personalized content in modern E-commerce, entertainment and social media platforms. Recommender Systems (RS) provide a critical solution to this problem by maintaining user engagement and satisfaction with personalized content.
Traditional RS techniques are often linear limiting the expressivity required to model complex user-item interactions and require extensive handcrafted features from domain experts. Deep learning demonstrated significant breakthroughs in solving problems that have alluded the artificial intelligence community for many years advancing state-of-the-art results in domains such as computer vision and natural language processing.
The recommender domain consists of heterogeneous and semantically rich data such as unstructured text (e.g. product descriptions), categorical attributes (e.g. genre of a movie), and user-item feedback (e.g. purchases). Deep learning can automatically capture the intricate structure of user preferences by encoding learned feature representations from high dimensional data.
In this thesis, we explore five novel applications of deep learning-based techniques to address top-n recommendation. First, we propose Collaborative Memory Network, which unifies the strengths of the latent factor model and neighborhood-based methods inspired by Memory Networks to address collaborative filtering with implicit feedback. Second, we propose Neural Semantic Personalized Ranking, a novel probabilistic generative modeling approach to integrate deep neural network with pairwise ranking for the item cold-start problem. Third, we propose Attentive Contextual Denoising Autoencoder augmented with a context-driven attention mechanism to integrate arbitrary user and item attributes. Fourth, we propose a flexible encoder-decoder architecture called Neural Citation Network, embodying a powerful max time delay neural network encoder augmented with an attention mechanism and author networks to address context-aware citation recommendation. Finally, we propose a generic framework to perform conversational movie recommendations which leverages transfer learning to infer user preferences from natural language. Comprehensive experiments validate the effectiveness of all five proposed models against competitive baseline methods and demonstrate the successful adaptation of deep learning-based techniques to the recommendation domain
Multimodal Recommender Systems in the Prediction of Disease Comorbidity
While deep-learning based recommender systems utilizing collaborative
filtering have been commonly used for recommendation in other domains, their
application in the medical domain have been limited. In addition to modeling
user-item interactions, we show that deep-learning based recommender systems
can be used to model subject-disease code interactions. Two novel applications
of deep learning-based recommender systems using Neural Collaborative Filtering
(NCF) and Deep Hybrid Filtering (DHF) were utilized for disease diagnosis based
on known past patient comorbidities. Two datasets, one incorporating all
subject-disease code pairs present in the MIMIC-III database, and the other
incorporating the top 50 most commonly occurring diseases, were used for
prediction. Accuracy and Hit Ratio@10 were utilized as metrics to estimate
model performance. The performance of the NCF model making use of the reduced
"top 50" ICD-9 code dataset was found to be lower (accuracy of ~80% and hit
ratio@10 of 35%) as compared to the performance of the NCF model trained on all
ICD-9 codes (accuracy of ~90% and hit ratio@10 of ~80%). Reasons for the
superior performance of the sparser dataset with all ICD codes can be mainly
attributed to the higher volume of data and the robustness of deep-learning
based recommender systems with modeling sparse data. Additionally, results from
the DHF models reflect better performance than the NCF models, with a better
accuracy of 94.4% and hit ratio@10 of 85.36%, reflecting the importance of the
incorporation of clinical note information. Additionally, compared to
literature reports utilizing primarily natural language processing-based
predictions for the task of ICD-9 code co-occurrence, the novel deep
learning-based recommender systems approach performed better. Overall, the deep
learning-based recommender systems have shown promise in predicting disease
comorbidity.Comment: 2022 Fourth International Conference on Transdisciplinary AI
(TransAI
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
Recommender systems have become fundamental building blocks of modern online
products and services, and have a substantial impact on user experience. In the
past few years, deep learning methods have attracted a lot of research, and are
now heavily used in modern real-world recommender systems. Nevertheless,
dealing with recommendations in the cold-start setting, e.g., when a user has
done limited interactions in the system, is a problem that remains far from
solved. Meta-learning techniques, and in particular optimization-based
meta-learning, have recently become the most popular approaches in the academic
research literature for tackling the cold-start problem in deep learning models
for recommender systems. However, current meta-learning approaches are not
practical for real-world recommender systems, which have billions of users and
items, and strict latency requirements. In this paper we show that it is
possible to obtaining similar, or higher, performance on commonly used
benchmarks for the cold-start problem without using meta-learning techniques.
In more detail, we show that, when tuned correctly, standard and widely adopted
deep learning models perform just as well as newer meta-learning models. We
further show that an extremely simple modular approach using common
representation learning techniques, can perform comparably to meta-learning
techniques specifically designed for the cold-start setting while being much
more easily deployable in real-world applications
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