5,364 research outputs found
Reciprocal Recommendation System for Online Dating
Online dating sites have become popular platforms for people to look for
potential romantic partners. Different from traditional user-item
recommendations where the goal is to match items (e.g., books, videos, etc)
with a user's interests, a recommendation system for online dating aims to
match people who are mutually interested in and likely to communicate with each
other. We introduce similarity measures that capture the unique features and
characteristics of the online dating network, for example, the interest
similarity between two users if they send messages to same users, and
attractiveness similarity if they receive messages from same users. A
reciprocal score that measures the compatibility between a user and each
potential dating candidate is computed and the recommendation list is generated
to include users with top scores. The performance of our proposed
recommendation system is evaluated on a real-world dataset from a major online
dating site in China. The results show that our recommendation algorithms
significantly outperform previously proposed approaches, and the collaborative
filtering-based algorithms achieve much better performance than content-based
algorithms in both precision and recall. Our results also reveal interesting
behavioral difference between male and female users when it comes to looking
for potential dates. In particular, males tend to be focused on their own
interest and oblivious towards their attractiveness to potential dates, while
females are more conscientious to their own attractiveness to the other side of
the line
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top- recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.Comment: In Proc. of 6th Australasian Data Mining Conference (AusDM), Gold
Coast, Australia, pages 195--202, 200
Personalised trails and learner profiling within e-learning environments
This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails
Hybrid group recommendations for a travel service
Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations
Recommender system to support comprehensive exploration of large scale scientific datasets
Bases de dados de entidades cientĂficas, como compostos quĂmicos, doenças e objetos astronĂłmicos, tĂȘm crescido em tamanho e complexidade, chegando a milhares de milhĂ”es de itens por base de dados. Os investigadores precisam de ferramentas novas e inovadoras para auxiliar na escolha desses itens. Este trabalho propĂ”e o uso de Sistemas de Recomendação para auxiliar os investigadores a encontrar itens de interesse. Identificamos como um dos maiores desafios para a aplicação de sistemas de recomendação em ĂĄreas cientĂficas a falta de conjuntos de dados padronizados e de acesso aberto com informaçÔes sobre as preferĂȘncias dos utilizadores. Para superar esse desafio, desenvolvemos uma metodologia denominada LIBRETTI - Recomendação Baseada em Literatura de Itens CientĂficos, cujo objetivo Ă© a criação de conjuntos de dados , relacionados com campos cientĂficos. Estes conjuntos de dados sĂŁo criados com base no principal recurso de conhecimento que a CiĂȘncia possui: a literatura cientĂfica. A metodologia LIBRETTI permitiu o desenvolvimento de novos algoritmos de recomendação especĂficos para vĂĄrios campos cientĂficos. AlĂ©m do LIBRETTI, as principais contribuiçÔes desta tese sĂŁo conjuntos de dados de recomendação padronizados nas ĂĄreas de Astronomia, QuĂmica e SaĂșde (relacionado com a doença COVID-19), um sistema de recomendação semĂąntica hĂbrido para compostos quĂmicos em conjuntos de dados de grande escala, uma abordagem hĂbrida baseada no enriquecimento sequencial (SeEn) para recomendaçÔes sequenciais, um pipeline baseado em semĂąntica de vĂĄrios campos para recomendar entidades biomĂ©dicas relacionadas com a doença COVID-19.Databases for scientific entities, such as chemical compounds, diseases and astronomical objects, are growing in size and complexity, reaching billions of items per database. Researchers need new and innovative tools for assisting the choice of these items. This work proposes the use of Recommender Systems approaches for helping researchers to find items of interest. We identified as one of the major challenges for applying RS in scientific fields the lack of standard and open-access datasets with information about the preferences of the users.
To overcome this challenge, we developed a methodology called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is to create datasets related to scientific fields. These datasets are created based on scientific literature, the major resource of knowledge that Science has. LIBRETTI methodology allowed the development and testing of new recommender algorithms specific for each field. Besides LIBRETTI, the main contributions of this thesis are standard and sequence-aware recommendation datasets in the fields of Astronomy, Chemistry, and Health (related to COVID-19 disease), a hybrid semantic recommender system for chemical compounds in large-scale datasets, a hybrid approach based on sequential enrichment (SeEn) for sequence-aware recommendations, a multi-field semantic-based pipeline for recommending biomedical entities related to COVID-19 disease
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