A reinforcement learning recommender system using bi-clustering and Markov Decision Process

Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning

CFMT: a collaborative filtering approach based on the nonnegative matrix factorization technique and trust relationships

peer reviewedAs a method of information filtering, the Recommender System (RS) has gained considerable popularity because of its efficiency and provision of the most superior numbers of useful items. A recommender system is a proposed solution to the information overload problem in social media and algorithms. Collaborative Filtering (CF) is a practical approach to the recommendation; however, it is characterized by cold start and data sparsity, the most severe barriers against providing accurate recommendations. Rating matrices are finely represented by Nonnegative Matrix Factorization (NMF) models, fundamental models in CF-based RSs. However, most NMF methods do not provide reasonable accuracy due to the dispersion of the rating matrix. As a result of the sparsity of data and problems concerning the cold start, information on the trust network among users is further utilized to elevate RS performance. Therefore, this study suggests a novel trust-based matrix factorization technique referred to as CFMT, which uses the social network data in the recommendation process by modeling user’s roles as trustees and trusters, given the trust network’s structural information. The proposed method seeks to lower the sparsity of the data and the cold start problem by integrating information sources including ratings and trust statements into the recommendation model, an attempt by which significant superiority over state-of-the-art approaches is demonstrated an empirical examination of real-world datasets

Atas das Oitavas Jornadas de Informática da Universidade de Évora

Atas das Oitavas Jornadas de Informática da Universidade de Évora realizadas em Março de 2018

Chatbots for Modelling, Modelling of Chatbots

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

Smart Fitness System: Training Programming

Sistemas de recomendação no geral estão a ser cada vez mais usados por empresas que procuram oferecer uma experiência de utilização mais individual e personalizada aos seus clientes. Obter feedback em transações de negócio online nunca foi tão fácil e acessível, o que apenas ajuda a catalisar a evolução dos sistemas de recomendação. Adicionalmente, o uso de dispositivos tecnológicos como smartphones e computadores, juntamente com a conexão à internet, estão também a crescer a um ritmo acelerado sem sinal de paragem em vista. Juntando-se a este grupo de indústrias em crescimento está a indústria fitness, que está a ficar cada vez mais popular. Com isto, mais e mais pessoas estão a começar a usar os dispositivos mencionados anteriormente em combinação com as suas atividades fitness, para aumentar o seu desempenho, monitorizar progresso, definir objetivos, entre outros. Consequentemente, o mercado para sistemas fitness (p.e. aplicações fitness) está a aumentar e já é bastante denso. No entanto, a qualidade associada com tais sistemas fica um pouco aquém tanto em termos de inovação como de funcionalidades essenciais. Como resultado disto, este projeto propôs uma solução – um sistema fitness sob a forma de uma aplicação móvel aliada a um poderoso sistema de recomendação. Este sistema é pretendido que providencie uma experiência mais individual e personalizada para qualquer tipo de utilizador fitness através da oferta de funcionalidades essenciais como registo e monitorização de informação, análise de progresso, e também através de funcionalidades inovadoras como a implementação de um sistema de recomendação capaz de sugerir tópicos relacionados com fitness (p.e. regimes de treino ou exercícios específicos) baseado em múltiplos fatores como os objetivos, características individuais e historial de cada utilizador. Além do mais, deve também oferecer um assistente pessoal virtual, onde os utilizadores podem expressar as suas questões e dúvidas, e tê-las respondidas instantaneamente por um chatbot. Durante o desenvolvimento foi decidido que um segundo sistema de recomendação seria necessário para melhorar o sistema no geral. Este, o sistema, depois de implementado, foi avaliado e pode ser concluído que o resultado foi um sucesso, tendo passado em todas as métricas definidas, exceto uma, com classificações médias nos questionários de satisfação acima de 4/5. O feedback obtido por um especialista no sistema de recomendação foi altamente vantajoso e no geral decentemente positivo, apenas com algumas questões que necessitam de melhoramento. Embora o sistema de recomendação inteligente não tenha conseguido ser testado com informação aplicável, a investigação e trabalho feito constituem uma mais valia caso mais tarde exista a possibilidade de aplicar dados reais.Recommender systems in general are increasingly becoming more exploited by companies who seek to provide a more individual and personalized user-experience to their customers. The fact that providing feedback on online business transactions is also becoming ever so easier only helps to catalyze the evolution of recommender systems. Moreover, the use of technological devices such as smartphones and computers, in conjunction with an internet connection, are also continuing to grow at a fast pace, with no slowing down in sight. Joining on this group of growing industries is the fitness sector, which is becoming increasingly popular. With this, more and more people are starting to use the aforementioned devices in combination with their fitness activities, to boost performance, monitor progress, define goals, among other things. Consequently, the market for fitness systems (i.e. fitness applications) is expanding and is already very dense. However, the associated quality with such systems falls short both in terms of innovation and even crucial features. As a result, this dissertation proposes a solution - an innovative fitness system in the form of a mobile application allied with a powerful recommender system. The system is intended to provide a more individual and personalized experience to any type of fitness user through the offering of crucial features including the log and monitor of information, progress analysis, and also through innovative features such as the implementation of a recommender system capable of making fitness-related suggestions (i.e. training regimens or specific exercises) based on multiple factors like the user’s individual goals, characteristics, and history. Additionally, it should also provide a personal virtual assistant, where users can express their questions and doubts and have them answered instantly by a chatbot. During development, it was decided that a second recommender system was required to improve the system as a whole. This, the system, after being implemented, was evaluated and it can be concluded that the result was a success, having passed in all the defined metrics, except one, with average classifications of 4/5 on the satisfaction inquiries. The feedback obtained from the expert on the recommender system was highly useful and, in general, decently positive, having only a few questions that need improvement. Even though the intelligent recommender system couldn’t be tested with applicable data, the investigation and work done constitute a great asset in case there’s the opportunity to employ real data

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Learning implicit recommenders from massive unobserved feedback

In this thesis we investigate implicit feedback techniques for real-world recommender systems. However, learning a recommender system from implicit feedback is very challenging, primarily due to the lack of negative feedback. While a common strategy is to treat the unobserved feedback (i.e., missing data) as a source of negative signal, the technical difficulties cannot be overlooked: (1) the ratio of positive to negative feedback in practice is highly imbalanced, and (2) learning through all unobserved feedback (which easily scales to billion level or higher) is computationally expensive. To effectively and efficiently learn recommender models from implicit feedback, two types of methods are presented, that is, negative sampling based stochastic gradient descent (NS-SGD) and whole sample based batch gradient descent (WS-BGD). Regarding the NS-SGD method, how to effectively sample informative negative examples to improve recommendation algorithms is investigated. More specifically, three learning models called Lambda Factorization Machines (lambdaFM), Boosting Factorization Machines (BoostFM) and Geographical Bayesian Personalized Ranking (GeoBPR) are described. While regarding the WS-BGD method, how to efficiently use all unobserved implicit feedback data rather than resorting to negative sampling is studied. A fast BGD learning algorithm is proposed, which can be applied to both basic collaborative filtering and content/context-aware recommendation settings. The last research work is on the session-based item recommendation, which is also an implicit feedback scenario. However, different from above four works based on shallow embedding models, we apply deep learning based sequence-to-sequence model to directly generate the probability distribution of next item. The proposed generative model can be applied to various sequential recommendation scenarios. To support the main arguments, extensive experiments are carried out based on real-world recommendation datasets. The proposed recommendation algorithms have achieved significant improvements in contrast with strong benchmark models. Moreover, these models can also serve as generic solutions and solid baselines for future implicit recommendation problems

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference