Improving the Dependability of Destination Recommendations using Information on Social Aspects

Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social attributes information of destinations is made a factor in the destination recommendation process.Content-based filtering; Recommender Systems; Ontology; Social Attributes, Destination recommendation

Evaluation of Famous Recommender Systems: A Comparative Analysis

In this era of the internet and with the easy availability of data at a very low cost, searching for information is growing at an exponential rate. So, it is now impossible to find the desired information without proper guidance. Here is the need of the recommendation system. The system will recommend which information relevant to the user according to their searching pattern. It will also explore hidden results with a minimal effect. The goal of this paper is to describe the famous recommendation systems which are used mostly and to explore what is the need of these kinds of systems and also what kind of technology has been used to provide better services to its users. Finally we would like to show how one recommendation system is different from another as per user need

Generalized Group Profiling for Content Customization

There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile sharing one or more characteristics with the user at hand. Personal profiles are often sparse, due to cold start problems and the fact that users typically search for new items or information, necessitating to back-off to customization, but group profiles often suffer from accidental features brought in by the unique individual contributing to the group. In this paper we propose a generalized group profiling approach that teases apart the exact contribution of the individual user level and the "abstract" group level by extracting a latent model that captures all, and only, the essential features of the whole group. Our main findings are the followings. First, we propose an efficient way of group profiling which implicitly eliminates the general and specific features from users' models in a group and takes out the abstract model representing the whole group. Second, we employ the resulting models in the task of contextual suggestion. We analyse different grouping criteria and we find that group-based suggestions improve the customization. Third, we see that the granularity of groups affects the quality of group profiling. We observe that grouping approach should compromise between the level of customization and groups' size.Comment: Short paper (4 pages) published in proceedings of ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR'16

Hybrid Recommender Systems: A Systematic Literature Review

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc

On the benefit of logic-based approach to learn pairwise comparisons

In recent years, many daily processes such as internet web searching, e-mail filtering, social media services, e-commerce have benefited from Machine Learning (ML) techniques. The implementation of ML techniques has been largely focused on black box methods where the general conclusions are not easily interpretable. Hence, the elaboration with other declarative software models to identify the correctness and completeness of the models is not easy to perform. On the other hand, the emerge of some logic-based machine learning approaches that can overcome such limitations with their white box methods has been proven to be well-suited for many software engineering tasks. In this paper, we propose the use of a logic-based approach to learn user preference in the form of pairwise comparisons. APARELL as a novel approach of inductive learning is able to model the user’s preferences in Description Logic(DL) and then build a model by generalising the concept for all examples given. This offers a rich, relational representation beyond the usual propositional domain, which is then can be used to produce a set of recommendations. A user study has been performed in our experiment to evaluate the implementation of pairwise preference recommender system when compared to a standard list interface. The result of the experiment shows that the pairwise interface was significantly better than the other interface in many ways

Software library for stream-based recommender systems

Tradicionalmente, um algoritmo de machine learning é capaz de aprender com dados, dado um conjunto tratado e construído anteriormente. Também é possível analisar esse conjunto de dados, usando técnicas de mineração de dados e extrair conclusões a partir dele. Ambos os conceitos têm inúmeras aplicações em todo o mundo, desde diagnósticos médicos até reconhecimento de fala ou mesmo consultas a mecanismos de pesquisa. No entanto, tradicionalmente, supõe-se que o conjunto de dados esteja disponível a todo o momento. Isso não é necessariamente verdade com os dados modernos pois os aplicativos de sistemas distribuídos recebem e processam milhões de fluxos de dados em uma fração de tempo limitado. Portanto, são necessárias técnicas para extrair e processar esses fluxos de dados, em um período de tempo limitado, com bons resultados e dimensionamento eficaz à medida que os dados aumentam. Um sistema específico de análise e previsão de conclusões futuras a partir de dados fornecidos são os sistemas de recomendação. Vários serviços online usam sistemas de recomendação para fornecer conteúdo personalizado a seus usuários. Em muitos casos, as recomendações são um dos geradores de tráfego mais eficazes nesses serviços. O problema reside em encontrar o melhor pequeno subconjunto de itens em um sistema que corresponda às preferências pessoais de cada usuário, através da análise de uma quantidade muito grande de dados históricos. Esse problema recebe mais atenção se for considerado um problema genérico, não específico, ou seja, se uma biblioteca for construída para que possa ser estendida e usada como uma ferramenta para ajudar a construir um sistema para um caso de uso específico. Podem-se distinguir soluções entre perfeitas ou estatisticamente semelhantes. Devido a grande quantidade de dados disponíveis, a decisão de reprocessar todos os dados, sempre que novos dados chegam, não seria viável; portanto, algoritmos incrementais são usados ​​para processar os dados recebidos e manter o modelo de recomendação atualizado. O objetivo real deste trabalho é implementar uma biblioteca que contenha e avalie essas abordagens incrementais para recomendações de que as atuais são específicas da tarefa.Traditionally, a machine learning algorithm is able to learn from data, given a previously built and treated data set. One can also analyze that data set, using data mining techniques, and draw conclusions from it. Both of these concepts have numerous world-wide applications, from medical diagnosis to speech recognition or even search engine queries. However, traditionally speaking, it is being assumed that the data set is available at all times. That is not necessarily true with modern data, as distributed systems applications receive and process millions of data streams on a limited time fraction. Therefore, there is a need for techniques to mine and process these data streams,on a limited time period with good results and effective scaling as data grows. One specific use case of analyzing and predicting future conclusions from given data, are recommendation systems.Several online services use recommender systems to deliver personalized content to their users.In many cases, recommendations are one of the most effective traffic generators in such services.The problem lies in finding the best small subset of items in a system that matches the personal preferences of each user, through the analysis of a very large amount of historical data. This problem gets more attention if it is considered as a generic problem, not as a specific one, that is,if a library is built so that it can be extended and used as a tool to help build a system for a specific use case. One can distinguish solutions between perfect ones or statistically similar ones. Due to the large amount of data available, the decision to reprocess all the data every time new data arrives, would not be feasible so, incremental algorithms are used to process incoming data and keeping the recommendation model updated. The real purpose of this work is to implement such a library which contains, and evaluates these incremental approaches to recommendation since current ones are task-specific