1,019 research outputs found
Recommendation Systems Based on Association Rule Mining for a Target Object by Evolutionary Algorithms
Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then the Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality
RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm
Nowadays, the recommendation is an important task in the decision-making process about the selection of items especially when item space is large, diverse, and constantly updating. As a challenge in the recent systems, the preference and interest of users change over time, and existing recommender systems do not evolve optimal clustering with sufficient accuracy over time. Moreover, the behavior history of the users is determined by their neighbours. The purpose of the time parameter for this system is to extend the time-based priority. This paper has been carried out a time-aware recommender systems based on memetic evolutionary clustering algorithm called RecMem for recommendations. In this system, clusters that evolve over time using the memetic evolutionary algorithm and extract the best clusters at every timestamp, and improve the memetic algorithm using the chaos criterion. The system provides appropriate suggestions to the user based on optimum clustering. The system uses optimal evolutionary clustering using item attributes for the cold-start item problem and demographic information for the cold start user problem. The results show that the proposed method has an accuracy of approximately 0.95, which is more effective than existing systems
EvoRecSys: Evolutionary framework for health and well-being recommender systems
Hugo Alcaraz-Herrera's PhD is supported by The Mexican Council of Science and Technology (Consejo Nacional de Ciencia y Tecnologia - CONACyT).In recent years, recommender systems have been employed in domains like ecommerce,
tourism, and multimedia streaming, where personalising users’ experience
based on their interactions is a fundamental aspect to consider. Recent recommender
system developments have also focused on well-being, yet existing solutions have
been entirely designed considering one single well-being aspect in isolation, such
as a healthy diet or an active lifestyle. This research introduces EvoRecSys, a novel
recommendation framework that proposes evolutionary algorithms as the main recommendation
engine, thereby modelling the problem of generating personalised
well-being recommendations as a multi-objective optimisation problem. EvoRecSys
captures the interrelation between multiple aspects of well-being by constructing configurable
recommendations in the form of bundled items with dynamic properties. The preferences and a predefined well-being goal by the user are jointly considered.
By instantiating the framework into an implemented model, we illustrate the use of
a genetic algorithm as the recommendation engine. Finally, this implementation has
been deployed as a Web application in order to conduct a users’ study.Consejo Nacional de Ciencia y Tecnologia (CONACyT
Evolutionary Approach for Building, Exploring and Recommending Complex Items With Application in Nutritional Interventions
Over the last few years, the ability of recommender systems to help us in different environments
has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition,
housing, or traveling. In this paper, we present a recommendation system capable of using different
input sources (data and knowledge-based) and producing a complex structured output. We have used an
evolutionary approach to combine several unitary items within a flexible structure and have built an initial
set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these
candidates to offer a final recommendation.We conclude with the application of this approach to the healthy
diet recommendation problem, addressing its strengths in this domain.Over the last few years, the ability of recommender systems to help us in different environments
has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition,
housing, or traveling. In this paper, we present a recommendation system capable of using different
input sources (data and knowledge-based) and producing a complex structured output. We have used an
evolutionary approach to combine several unitary items within a flexible structure and have built an initial
set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these
candidates to offer a final recommendation.We conclude with the application of this approach to the healthy
diet recommendation problem, addressing its strengths in this domainEuropean Union (Stance4Health) under Grant 816303Ministerio de Ciencia e
Innovación under Grant PID2021-123960OB-I00MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de
Investigacion)/10.13039/501100011033ERDF (European Regional Development Fund)A way of making Europe.
And in part under Grant TED2021-129402B-C21 funded by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de
Investigacion)/10.13039/501100011033European Union NextGenerationEU/PRTR (Plan de Recuperación,
Transformación y Resiliencia)‘Program of Information and Communication technologies’’ at the University of Granad
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Innovative food recommendation systems: a machine learning approach
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecommendation systems employ users history data records to predict their preference,
and have been widely used in diverse fields including biology, e-commerce, and healthcare.
Traditional recommendation techniques include content-based, collaborative-based and
hybrid methods but not all real-world problems can be best addressed by these classical
recommendation techniques. Food recommendation is one such challenging problem where
there is an urgent need to use novel recommendation systems in assisting people to select
healthy, balanced and personalized food plans. In this thesis, we make several advances in
food recommendation systems using innovative machine learning methods. First, a novel
recommendation approach is proposed by transforming an original recommendation problem
into a many-objective optimisation one that contains several different objectives resulting in
more balanced recommendations. Second, a unified approach to designing sequence-based
personalised food recommendation systems is investigated to accommodate dynamic user
behaviours. Third, a new food recommendation approach is developed with a temporal
dependent graph neural network and data augmentation techniques leading to more accurate
and robust recommendations. The experimental results show that these proposed approaches
have not only provided a more balanced and accurate way of recommending food than the
traditional methods but also led to promising areas for future research
Automated Negotiation Among Web Services
Software as a service is well accepted software deployment and distribution model that is grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of these services in a composite system. It allows users to automatically find and bind these services, as to maximize the productivity of their composed systems, meeting both functional and non-functional requirements. In this paper we present a framework for modeling the dependency relationship of different Quality of Service parameters of a component service. Our proposed approach considers the different invocation patterns of component services in the system and models the dependency relationship for optimum values of these QoS parameters. We present a service composition framework that models the dependency relations ship among component services and uses the global QoS for service selection
Contextual Model-Based Collaborative Filtering for Recommender Systems
Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore that user preferences can change according to context, resulting in recommendations that do not fit user interests. Context-aware models have been proposed to address this issue, but these models have problems of their own. The ever-increasing speed at which data are generated presents a scalability challenge for single-model approaches. Moreover, the complexity of these models prevents small players from adapting and implementing contextual models that meet their needs.
This thesis addresses these issues by proposing the (CF)2 architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering (CF) models. CF has been available for decades, and its methods and benefits have been extensively discussed and implemented. Moreover, the use of context as filtering criteria for local learning addresses the scalability issues caused by the use of large datasets. Therefore, the proposed architecture enables the creation of contextual recommendations using several models instead of one, with each model representing a context. In addition, the architecture is implemented and evaluated in two case studies. Results show that contextual models trained with a small fraction of the data resulted in similar or better accuracy compared to CF models trained with the total dataset. Moreover, experiments indicate that local learning using contextual information outperforms random selection in accuracy and in training time
Multicriteria Evaluation for Top-k and Sequence-based Recommender Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen
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