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The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge
Python Library for Consumer Decision Support System with Automatic Identification of Preferences
The development of information systems (IS) has increased in the e-commerce field. The need for continuous improvement of decision support systems implies the integration of multiple methodologies such as expert knowledge, data mining, big data, artificial intelligence, and multicriteria decision analysis (MCDA) methods. Artificial intelligence algorithms have proven their effectiveness as an engine for data-driven information systems. MCDA methods demonstrated usefulness in domains dealing with multiple dimensions. One of the most critical points of any MCDA procedure is criteria weighting using subjective or objective methods. However, both approaches have several limitations when there is a need to map the preferences of unavailable experts. EVO-SPOTIS library integrating a stochastic evolutionary algorithm with the MCDA method, introduced in this paper, attempts to address this problem. In this approach, the Differential Evolution (DE) algorithm is used to identify decision-makers’ preferences based on datasets evaluated by experts in the past. The Stable Preference Ordering Towards Ideal Solution (SPOTIS) method is used to compute the DE objective function’s values and perform the final evaluation of alternatives using the identified weights. Results confirm the high potential of the library for identification preferences and modeling customer behavior
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sequential recommendation demonstrates the capability to recommend items by
modeling the sequential behavior of users. Traditional methods typically treat
users as sequences of items, overlooking the collaborative relationships among
them. Graph-based methods incorporate collaborative information by utilizing
the user-item interaction graph. However, these methods sometimes face
challenges in terms of time complexity and computational efficiency. To address
these limitations, this paper presents AutoSeqRec, an incremental
recommendation model specifically designed for sequential recommendation tasks.
AutoSeqRec is based on autoencoders and consists of an encoder and three
decoders within the autoencoder architecture. These components consider both
the user-item interaction matrix and the rows and columns of the item
transition matrix. The reconstruction of the user-item interaction matrix
captures user long-term preferences through collaborative filtering. In
addition, the rows and columns of the item transition matrix represent the item
out-degree and in-degree hopping behavior, which allows for modeling the user's
short-term interests. When making incremental recommendations, only the input
matrices need to be updated, without the need to update parameters, which makes
AutoSeqRec very efficient. Comprehensive evaluations demonstrate that
AutoSeqRec outperforms existing methods in terms of accuracy, while showcasing
its robustness and efficiency.Comment: 10 pages, accepted by CIKM 202
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
Using Differential Evolution in order to create a personalized list of recommended items
The recommendation systems are used to suggest new, still not discovered items to users. At the moment, in order to achieve the best quality of the generated recommendations, users and their choices in the system
must be analyzed to create a certain profile of preferences for a given user in order to adjust the generated recommendation to his personal taste. This article will present a recommendation system, which based on
the Differential Evolution (DE) algorithm will learn the ranking function while directly optimizing the average precision (AP) for the selected user in the system. To achieve that, items are represented through a feature vectors generated using user-item matrix factorization. The experiments have been conducted on a popular and widely available public dataset MovieLens, and show that our approach in certain situations can significantly improve the quality of the generated recommendations. Results of experiments are compared with other techniques
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
PICAE – Intelligent publication of audiovisual and editorial contents
The development in internet infrastructure and technology in last tow decades have given users and retailers the possibility to purchase and sell items online. This has of course broadened the horizons of what products can be offered outside of the traditional trading sense, to the point where virtually any product can be offered. These massive online markets have had a considerable impact on the habits of consumers, providing them access to a greater variety of products and information on these goods. This variety has made online commerce into a multi-billion dollar industry but it has also put the customer in a position where it is getting increasingly difficult to select the products that best fit their individual needs. In the same vein, the rise of both availability and the amounts of data that computers have been able to process in the last decades have allowed for many solutions that are computationally expensive to exist, and recommender systems are no exception. These systems are the perfect tools to overcome the information overload problem since they provide automated and personalized suggestions to consumers. The PICAE project tackles the recommendation problem in the audiovisual sector. The vast amount of audiovisual content that is available nowadays to the user can be overwhelming, which is why recommenders have been increasingly growing in popularity in this sector ---Netflix being the biggest example. PICAE seeks to provide insightful and personalized recommendations to users in a public TV setting. The PICAE project develops new models and analytical tools for recommending audiovisual and editorial content with the aim of improving the user experience, based on their profile and environment, and the level of satisfaction and loyalty. These new tools represent a qualitative improvement in the state of the art of television and editorial content recommendation. On the other hand, the project also improves the digital consumption index of these contents based on the identification of products that these new forms of consumption demand and how they must be produced, distributed and promoted to respond to the needs of this emerging market. The main challenge of the PICAE project is to resolve two differentiating aspects with respect to other existing solutions such as: variety and dynamic contents that requires a real-time analysis of the recommendation and the lack of available information about the user, who in these areas is reluctant to register, making it difficult to identify in multi-device consumption. This document will explain the contributions made in the development of the project, which can be divided in two: the development of the project, which can be divided in two: the development of a recommender system that takes into account information of both users and items and a deep analysis of the current metrics used to assess the performance of a recommender system
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
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