Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

MOOCs Meet Measurement Theory: A Topic-Modelling Approach

This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.Comment: 12 pages, 9 figures; accepted into AAAI'201

Personalised Visual Art Recommendation by Learning Latent Semantic Representations

In Recommender systems, data representation techniques play a great role as they have the power to entangle, hide and reveal explanatory factors embedded within datasets. Hence, they influence the quality of recommendations. Specifically, in Visual Art (VA) recommendations the complexity of the concepts embodied within paintings, makes the task of capturing semantics by machines far from trivial. In VA recommendation, prominent works commonly use manually curated metadata to drive recommendations. Recent works in this domain aim at leveraging visual features extracted using Deep Neural Networks (DNN). However, such data representation approaches are resource demanding and do not have a direct interpretation, hindering user acceptance. To address these limitations, we introduce an approach for Personalised Recommendation of Visual arts based on learning latent semantic representation of paintings. Specifically, we trained a Latent Dirichlet Allocation (LDA) model on textual descriptions of paintings. Our LDA model manages to successfully uncover non-obvious semantic relationships between paintings whilst being able to offer explainable recommendations. Experimental evaluations demonstrate that our method tends to perform better than exploiting visual features extracted using pre-trained Deep Neural Networks.Comment: Accepted at SMAP202

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Novel online Recommendation algorithm for Massive Open Online Courses (NoR-MOOCs)

Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics

Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art Recommendation

With the advent of digital media, the availability of art content has greatly expanded, making it increasingly challenging for individuals to discover and curate works that align with their personal preferences and taste. The task of providing accurate and personalised Visual Art (VA) recommendations is thus a complex one, requiring a deep understanding of the intricate interplay of multiple modalities such as images, textual descriptions, or other metadata. In this paper, we study the nuances of modalities involved in the VA domain (image and text) and how they can be effectively harnessed to provide a truly personalised art experience to users. Particularly, we develop four fusion-based multimodal VA recommendation pipelines and conduct a large-scale user-centric evaluation. Our results indicate that early fusion (i.e, joint multimodal learning of visual and textual features) is preferred over a late fusion of ranked paintings from unimodal models (state-of-the-art baselines) but only if the latent representation space of the multimodal painting embeddings is entangled. Our findings open a new perspective for a better representation learning in the VA RecSys domain

Model for analyzing course description using LDA topic modeling

This study demonstrates a way to generate a Topic model using LDA (Latent Dirichlet Allocation) topic modeling for the courses of multiple universities in the USA, which is relatively significant. This model will specifically be able to differentiate the course structure between various universities, such as the University of North Carolina at Wilmington, the University of North Texas, the University of South Carolina, and the University of Western Carolina. This model will help find the related courses of a selected department of study, or so they thought. The LDA (Latent Dirichlet Allocation) topic model is used to infer topics from the content in the university course description. Further, this study showed how to generate a Topic model using LDA (Latent Dirichlet Allocation) topic modeling for the courses of multiple universities in the USA. This study will: Explain how to Infer topics from the corpora consisting of various universities’ text of course details; Helps to find out the related courses of a selected department of study in a big way; Group the topics into different communities by calculating the Modularity with the help of the Louvain method; Analyze how the courses are related to the topics, for the most part subtly inferred for each University; For a selected Department of study, see what all courses belongs to this department with the help of topics generated. This study helps us to identify the courses which have a relation with a selected department of study. The graph representations mainly included in this paper will generally explain our Approach

Diseño de un aplicativo web que recomiende asignaturas electivas a estudiantes de ingeniería industrial de la Pontificia Universidad Javeriana

Las asignaturas electivas buscan el desarrollo integral de los estudiantes, la Pontificia Universidad Javeriana ofrece una gran cantidad de opciones, lo que, usualmente, dificulta el proceso de elección e inscripción de este tipo de asignaturas. Este problema lleva a los estudiantes a retirar las asignaturas o ver asignaturas electivas cuyo contenido no es de su agrado. Estas situaciones no promueven, necesariamente, el proceso de formación integral. Por esta razón, se hace necesaria una herramienta que recomiende asignaturas electivas a los estudiantes, según sus preferencias de aprendizaje. Este problema se va abordar a través de un algoritmo de recomendación mixto que base su predicción en el historial académico de los estudiantes. Se realizó una implementación web de este sistema de recomendación para facilitar el proceso de elección e inscripción de asignaturas electivas, promoviendo, la esencia de la educación integral.Elective subjects seek the student’s integral development, the Pontificia Universidad Javeriana offers a great amount of options, which, usually, makes the election and inscription processes of this kind of subjects more difficult. This problem leads the students to withdraw the subjects or to enroll into subjects whose contents do not reflect their likings. These situations don’t necessarily promote the process of integral formation. This reason makes necessary a tool that recommends elective subjects to students, according to their learning preferences. And the problem will be addressed through a hybrid recommender algorithm that bases its prediction on students' academic records. A web implementation of this recommendation system was made to facilitate the process of electing and registering elective subjects, promoting the essence of integral education.Ingeniero (a) IndustrialPregrad