53 research outputs found
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Combinatorial features are essential for the success of many commercial
models. Manually crafting these features usually comes with high cost due to
the variety, volume and velocity of raw data in web-scale systems.
Factorization based models, which measure interactions in terms of vector
product, can learn patterns of combinatorial features automatically and
generalize to unseen features as well. With the great success of deep neural
networks (DNNs) in various fields, recently researchers have proposed several
DNN-based factorization model to learn both low- and high-order feature
interactions. Despite the powerful ability of learning an arbitrary function
from data, plain DNNs generate feature interactions implicitly and at the
bit-wise level. In this paper, we propose a novel Compressed Interaction
Network (CIN), which aims to generate feature interactions in an explicit
fashion and at the vector-wise level. We show that the CIN share some
functionalities with convolutional neural networks (CNNs) and recurrent neural
networks (RNNs). We further combine a CIN and a classical DNN into one unified
model, and named this new model eXtreme Deep Factorization Machine (xDeepFM).
On one hand, the xDeepFM is able to learn certain bounded-degree feature
interactions explicitly; on the other hand, it can learn arbitrary low- and
high-order feature interactions implicitly. We conduct comprehensive
experiments on three real-world datasets. Our results demonstrate that xDeepFM
outperforms state-of-the-art models. We have released the source code of
xDeepFM at \url{https://github.com/Leavingseason/xDeepFM}.Comment: 10 page
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
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become
integral for providing personalized suggestions and overcoming information
overload. However, their practical deployment often encounters "dirty" data,
where noise or malicious information can lead to abnormal recommendations.
Research on improving recommender systems' robustness against such dirty data
has thus gained significant attention. This survey provides a comprehensive
review of recent work on recommender systems' robustness. We first present a
taxonomy to organize current techniques for withstanding malicious attacks and
natural noise. We then explore state-of-the-art methods in each category,
including fraudster detection, adversarial training, certifiable robust
training against malicious attacks, and regularization, purification,
self-supervised learning against natural noise. Additionally, we summarize
evaluation metrics and common datasets used to assess robustness. We discuss
robustness across varying recommendation scenarios and its interplay with other
properties like accuracy, interpretability, privacy, and fairness. Finally, we
delve into open issues and future research directions in this emerging field.
Our goal is to equip readers with a holistic understanding of robust
recommender systems and spotlight pathways for future research and development
Exploring attributes, sequences, and time in Recommender Systems: From classical to Point-of-Interest recommendation
Tesis Doctoral inĆ©dita leĆda en la Universidad AutĆ³noma de Madrid, Escuela PolitĆ©cnica Superior, Departamento de Ingenieria InformĆ”tica. Fecha de lectura: 08-07-2021Since the emergence of the Internet and the spread of digital communications
throughout the world, the amount of data stored on the Web has been
growing exponentially. In this new digital era, a large number of companies
have emerged with the purpose of ltering the information available on the
web and provide users with interesting items. The algorithms and models
used to recommend these items are called Recommender Systems. These
systems are applied to a large number of domains, from music, books, or
movies to dating or Point-of-Interest (POI), which is an increasingly popular
domain where users receive recommendations of di erent places when
they arrive to a city.
In this thesis, we focus on exploiting the use of contextual information, especially
temporal and sequential data, and apply it in novel ways in both
traditional and Point-of-Interest recommendation. We believe that this type
of information can be used not only for creating new recommendation models
but also for developing new metrics for analyzing the quality of these
recommendations. In one of our rst contributions we propose di erent
metrics, some of them derived from previously existing frameworks, using
this contextual information. Besides, we also propose an intuitive algorithm
that is able to provide recommendations to a target user by exploiting the
last common interactions with other similar users of the system.
At the same time, we conduct a comprehensive review of the algorithms
that have been proposed in the area of POI recommendation between 2011
and 2019, identifying the common characteristics and methodologies used.
Once this classi cation of the algorithms proposed to date is completed, we
design a mechanism to recommend complete routes (not only independent
POIs) to users, making use of reranking techniques. In addition, due to the
great di culty of making recommendations in the POI domain, we propose
the use of data aggregation techniques to use information from di erent
cities to generate POI recommendations in a given target city.
In the experimental work we present our approaches on di erent datasets
belonging to both classical and POI recommendation. The results obtained
in these experiments con rm the usefulness of our recommendation proposals,
in terms of ranking accuracy and other dimensions like novelty, diversity,
and coverage, and the appropriateness of our metrics for analyzing temporal
information and biases in the recommendations producedDesde la aparici on de Internet y la difusi on de las redes de comunicaciones
en todo el mundo, la cantidad de datos almacenados en la red ha crecido
exponencialmente. En esta nueva era digital, han surgido un gran n umero
de empresas con el objetivo de ltrar la informaci on disponible en la red
y ofrecer a los usuarios art culos interesantes. Los algoritmos y modelos
utilizados para recomendar estos art culos reciben el nombre de Sistemas de
Recomendaci on. Estos sistemas se aplican a un gran n umero de dominios,
desde m usica, libros o pel culas hasta las citas o los Puntos de Inter es (POIs,
en ingl es), un dominio cada vez m as popular en el que los usuarios reciben
recomendaciones de diferentes lugares cuando llegan a una ciudad.
En esta tesis, nos centramos en explotar el uso de la informaci on contextual,
especialmente los datos temporales y secuenciales, y aplicarla de forma novedosa
tanto en la recomendaci on cl asica como en la recomendaci on de POIs.
Creemos que este tipo de informaci on puede utilizarse no s olo para crear
nuevos modelos de recomendaci on, sino tambi en para desarrollar nuevas
m etricas para analizar la calidad de estas recomendaciones. En una de
nuestras primeras contribuciones proponemos diferentes m etricas, algunas
derivadas de formulaciones previamente existentes, utilizando esta informaci
on contextual. Adem as, proponemos un algoritmo intuitivo que es
capaz de proporcionar recomendaciones a un usuario objetivo explotando
las ultimas interacciones comunes con otros usuarios similares del sistema.
Al mismo tiempo, realizamos una revisi on exhaustiva de los algoritmos que
se han propuesto en el a mbito de la recomendaci o n de POIs entre 2011 y
2019, identi cando las caracter sticas comunes y las metodolog as utilizadas.
Una vez realizada esta clasi caci on de los algoritmos propuestos hasta la
fecha, dise~namos un mecanismo para recomendar rutas completas (no s olo
POIs independientes) a los usuarios, haciendo uso de t ecnicas de reranking.
Adem as, debido a la gran di cultad de realizar recomendaciones en el
ambito de los POIs, proponemos el uso de t ecnicas de agregaci on de datos
para utilizar la informaci on de diferentes ciudades y generar recomendaciones
de POIs en una determinada ciudad objetivo.
En el trabajo experimental presentamos nuestros m etodos en diferentes
conjuntos de datos tanto de recomendaci on cl asica como de POIs. Los
resultados obtenidos en estos experimentos con rman la utilidad de nuestras
propuestas de recomendaci on en t erminos de precisi on de ranking y de
otras dimensiones como la novedad, la diversidad y la cobertura, y c omo de
apropiadas son nuestras m etricas para analizar la informaci on temporal y
los sesgos en las recomendaciones producida
Assessing and improving recommender systems to deal with user cold-start problem
Recommender systems are in our everyday life. The recommendation methods have as
main purpose to predict preferences for new items based on userÅ s past preferences. The
research related to this topic seeks among other things to discuss user cold-start problem,
which is the challenge of recommending to users with few or no preferences records.
One way to address cold-start issues is to infer the missing data relying on side information.
Side information of different types has been explored in researches. Some
studies use social information combined with usersÅ preferences, others user click behavior,
location-based information, userÅ s visual perception, contextual information, etc. The
typical approach is to use side information to build one prediction model for each cold
user. Due to the inherent complexity of this prediction process, for full cold-start user in
particular, the performance of most recommender systems falls a great deal. We, rather,
propose that cold users are best served by models already built in system.
In this thesis we propose 4 approaches to deal with user cold-start problem using
existing models available for analysis in the recommender systems. We cover the follow
aspects:
o Embedding social information into traditional recommender systems: We investigate
the role of several social metrics on pairwise preference recommendations and
provide the Ärst steps towards a general framework to incorporate social information
in traditional approaches.
o Improving recommendation with visual perception similarities: We extract networks
connecting users with similar visual perception and use them to come up with
prediction models that maximize the information gained from cold users.
o Analyzing the beneÄts of general framework to incorporate networked information
into recommender systems: Representing different types of side information as a
user network, we investigated how to incorporate networked information into recommender
systems to understand the beneÄts of it in the context of cold user
recommendation.
o Analyzing the impact of prediction model selection for cold users: The last proposal
consider that without side information the system will recommend to cold users
based on the switch of models already built in system.
We evaluated the proposed approaches in terms of prediction quality and ranking
quality in real-world datasets under different recommendation domains. The experiments
showed that our approaches achieve better results than the comparison methods.Tese (Doutorado)Sistemas de recomendaĆ§Ć£o fazem parte do nosso dia-a-dia. Os mĆ©todos usados nesses
sistemas tem como objetivo principal predizer as preferĆŖncias por novos itens baseado no
perÄl do usuĆ”rio. As pesquisas relacionadas a esse tĆ³pico procuram entre outras coisas
tratar o problema do cold-start do usuĆ”rio, que Ć© o desaÄo de recomendar itens para
usuĆ”rios que possuem poucos ou nenhum registro de preferĆŖncias no sistema.
Uma forma de tratar o cold-start do usuĆ”rio Ć© buscar inferir as preferĆŖncias dos usuĆ”rios
a partir de informaƧƵes adicionais. Dessa forma, informaƧƵes adicionais de diferentes tipos
podem ser exploradas nas pesquisas. Alguns estudos usam informaĆ§Ć£o social combinada
com preferĆŖncias dos usuĆ”rios, outros se baseiam nos clicks ao navegar por sites Web,
informaĆ§Ć£o de localizaĆ§Ć£o geogrĆ”Äca, percepĆ§Ć£o visual, informaĆ§Ć£o de contexto, etc. A
abordagem tĆpica desses sistemas Ć© usar informaĆ§Ć£o adicional para construir um modelo
de prediĆ§Ć£o para cada usuĆ”rio. AlĆ©m desse processo ser mais complexo, para usuĆ”rios
full cold-start (sem preferĆŖncias identiÄcadas pelo sistema) em particular, a maioria dos
sistemas de recomendaĆ§Ć£o apresentam um baixo desempenho. O trabalho aqui apresentado,
por outro lado, propƵe que novos usuĆ”rios receberĆ£o recomendaƧƵes mais acuradas
de modelos de prediĆ§Ć£o que jĆ” existem no sistema.
Nesta tese foram propostas 4 abordagens para lidar com o problema de cold-start
do usuĆ”rio usando modelos existentes nos sistemas de recomendaĆ§Ć£o. As abordagens
apresentadas trataram os seguintes aspectos:
o InclusĆ£o de informaĆ§Ć£o social em sistemas de recomendaĆ§Ć£o tradicional: foram investigados
os papĆ©is de vĆ”rias mĆ©tricas sociais em um sistema de recomendaĆ§Ć£o de
preferĆŖncias pairwise fornecendo subsidĆos para a deÄniĆ§Ć£o de um framework geral
para incluir informaĆ§Ć£o social em abordagens tradicionais.
o Uso de similaridade por percepĆ§Ć£o visual: usando a similaridade por percepĆ§Ć£o
visual foram inferidas redes, conectando usuƔrios similares, para serem usadas na
seleĆ§Ć£o de modelos de prediĆ§Ć£o para novos usuĆ”rios.
o AnĆ”lise dos benefĆcios de um framework geral para incluir informaĆ§Ć£o de redes
de usuĆ”rios em sistemas de recomendaĆ§Ć£o: representando diferentes tipos de informaĆ§Ć£o
adicional como uma rede de usuƔrios, foi investigado como as redes de
usuĆ”rios podem ser incluĆdas nos sistemas de recomendaĆ§Ć£o de maneira a beneÄciar
a recomendaĆ§Ć£o para usuĆ”rios cold-start.
o AnĆ”lise do impacto da seleĆ§Ć£o de modelos de prediĆ§Ć£o para usuĆ”rios cold-start:
a Ćŗltima abordagem proposta considerou que sem a informaĆ§Ć£o adicional o sistema
poderia recomendar para novos usuƔrios fazendo a troca entre os modelos jƔ
existentes no sistema e procurando aprender qual seria o mais adequado para a
recomendaĆ§Ć£o.
As abordagens propostas foram avaliadas em termos da qualidade da prediĆ§Ć£o e da
qualidade do ranking em banco de dados reais e de diferentes domĆnios. Os resultados
obtidos demonstraram que as abordagens propostas atingiram melhores resultados que os
mƩtodos do estado da arte
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č©²å½Doctor of InformaticsKyoto UniversityDFA
Recommended from our members
Patient Record Summarization Through Joint Phenotype Learning and Interactive Visualization
Complex patient are becoming more and more of a challenge to the health care system given the amount of care they require and the amount of documentation needed to keep track of their state of health and treatment. Record keeping using the EHR makes this easier but mounting amounts of patient data also means that clinicians are faced with information overload. Information overload has been shown to have deleterious effects on care, with increased safety concerns due to missed information. Patient record summarization has been a promising mitigator for information overload. Subsequently, a lot of research has been dedicated to record summarization since the introduction of EHRs. In this dissertation we examine whether unsupervised inference methods can derive patient problem-oriented summaries, that are robust to different patients. By grounding our experiments with HIV patients we leverage the data of a group of patients that are similar in that they share one common disease (HIV) but also exhibit complex histories of diverse comorbidities. Using a user-centered, iterative design process, we design an interactive, longitudinal patient record summarization tool, that leverages automated inferences about the patient's problems. We find that unsupervised, joint learning of problems using correlated topic models, adapted to handle the multiple data types (structured and unstructured) of the EHR, is successful in identifying the salient problems of complex patients. Utilizing interactive visualization that exposes inference results to users enables them to make sense of a patient's problems over time and to answer questions about a patient more accurately and faster than using the EHR alone
Vision for Social Robots: Human Perception and Pose Estimation
In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene.
The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention.
First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a personās face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error.
Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images.
Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p
Enhance Representation Learning of Clinical Narrative with Neural Networks for Clinical Predictive Modeling
Medicine is undergoing a technological revolution. Understanding human health from clinical data has major challenges from technical and practical perspectives, thus prompting methods that understand large, complex, and noisy data. These methods are particularly necessary for natural language data from clinical narratives/notes, which contain some of the richest information on a patient. Meanwhile, deep neural networks have achieved superior performance in a wide variety of natural language processing (NLP) tasks because of their capacity to encode meaningful but abstract representations and learn the entire task end-to-end. In this thesis, I investigate representation learning of clinical narratives with deep neural networks through a number of tasks ranging from clinical concept extraction, clinical note modeling, and patient-level language representation. I present methods utilizing representation learning with neural networks to support understanding of clinical text documents.
I first introduce the notion of representation learning from natural language processing and patient data modeling. Then, I investigate word-level representation learning to improve clinical concept extraction from clinical notes. I present two works on learning word representations and evaluate them to extract important concepts from clinical notes. The first study focuses on cancer-related information, and the second study evaluates shared-task data. The aims of these two studies are to automatically extract important entities from clinical notes. Next, I present a series of deep neural networks to encode hierarchical, longitudinal, and contextual information for modeling a series of clinical notes. I also evaluate the models by predicting clinical outcomes of interest, including mortality, length of stay, and phenotype predictions. Finally, I propose a novel representation learning architecture to develop a generalized and transferable language representation at the patient level. I also identify pre-training tasks appropriate for constructing a generalizable language representation. The main focus is to improve predictive performance of phenotypes with limited data, a challenging task due to a lack of data.
Overall, this dissertation addresses issues in natural language processing for medicine, including clinical text classification and modeling. These studies show major barriers to understanding large-scale clinical notes. It is believed that developing deep representation learning methods for distilling enormous amounts of heterogeneous data into patient-level language representations will improve evidence-based clinical understanding. The approach to solving these issues by learning representations could be used across clinical applications despite noisy data. I conclude that considering different linguistic components in natural language and sequential information between clinical events is important. Such results have implications beyond the immediate context of predictions and further suggest future directions for clinical machine learning research to improve clinical outcomes. This could be a starting point for future phenotyping methods based on natural language processing that construct patient-level language representations to improve clinical predictions. While significant progress has been made, many open questions remain, so I will highlight a few works to demonstrate promising directions
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