RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

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

Algoritmo Híbrido de Recomendação

Nesta era tecnológica em que nos encontramos há cada vez mais informação disponível na internet, mas grande parte dessa informação não é relevante. Isto leva à necessidade de criar maneiras de filtrar informação, de forma a reduzir o tempo de recolha de informação útil. Esta necessidade torna o uso de sistemas de recomendação muito apelativo, visto estes personalizarem as pesquisas de forma a ajudar os seus utilizadores a fazer escolhas mais informadas. Os sistemas de recomendação procuram recomendar os itens mais relevantes aos seus utilizadores, no entanto necessitam de informação sobre os utilizadores e os itens, de forma a melhor os poder organizar e categorizar. Há vários tipos de sistemas de recomendação, cada um com as suas forças e fraquezas. De modo a superar as limitações destes sistemas surgiram os sistemas de recomendação híbridos, que procuram combinar características dos diferentes tipos de sistemas de recomendação de modo a reduzir, ou eliminar, as suas fraquezas. Uma das limitações dos sistemas de recomendação acontece quando o próprio sistema não tem informação suficiente para fazer recomendações. Esta limitação tem o nome de Cold Start e pode focar-se numa de duas áreas: quando a falta de informação vem do utilizador, conhecida como User Cold Start; e quando a falta de informação vem de um item, conhecida como Item Cold Start. O foco desta dissertação é no User Cold Start, nomeadamente na criação de um sistema de recomendação híbrido capaz de lidar com esta situação. A abordagem apresentada nesta dissertação procura combinar a segmentação de clientes com regras de associação. O objetivo passa por descobrir os utilizadores mais similares aos utilizadores numa situação de Cold Start e, através dos itens avaliados pelos utilizadores mais similares, recomendar os itens considerados mais relevantes, obtidos através de regras de associação. O algoritmo híbrido apresentado nesta dissertação procura e classifica todos os tipos de utilizadores. Quando um utilizador numa situação de Cold Start está à procura de recomendações, o sistema encontra itens para recomendar através da aplicação de regras de associação a itens avaliados por utilizadores no mesmo grupo que o utilizador na situação de Cold Start, cruzando essas regras com os itens avaliados por este último e apresentando as recomendações com base no resultado.Recommender systems, or recommenders, are a way to filter the useful information from the data, in this age where there is a lot of available data. A recommender system’s purpose is to recommend relevant items to users, and to do that, it requires information on both, data from users and from items, to better organise and categorise both of them. There are several types of recommenders, each best suited for a specific purpose, and with specific weaknesses. Then there are hybrid recommenders, made by combining one or more types of recommenders in a way that each type supresses, or at least limits, the weaknesses of the other types. A very important weakness of recommender systems occurs when the system doesn’t have enough information about something and so, it cannot make a recommendation. This problem known as a Cold Start problem is addressed in this thesis. There are two types of Cold Start problems: those where the lack of information comes from a user (User Cold Start) and those where it comes from an item (Item Cold Start). This thesis’ main focus is on User Cold Start problems. A novel approach is introduced in this thesis which combines clients’ segmentation with association rules. The goal is first, finding the most similar users to cold start users and then, with the items rated by these similar users, recommend those that are most suitable, which are gotten through association rules. The hybrid algorithm presented in this thesis finds and classifies all users’ types. When a user in a Cold Start situation is looking for recommendations, the system finds the items to recommend to him by applying association rules to the items evaluated by users in the same user group as the Cold Start user, crossing them with the few items evaluated by the Cold Start user and finally making its recommendations based on that