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

Online engagement for a healthier you: A Case Study of Web-based Supermarket Health Program

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Obesity is a growing problem affecting millions of people. Various behavior change programs have been designed to reduce its prevalence. An Australian supermarket has recently run a web-based health program to motivate people to eat healthily and do more physical activity. The program offered discounts on fresh products and a website, HealthierU, providing interactive support tools for participants. The stakeholders desire to evaluate if the program is effective and if the supporting website is useful to facilitate behavior changes. To answer these questions, in this work we propose a method to: (1) model individual purchase rate from sparse recorded transactions through a mixture of Non-Homogeneous Poisson Processes (NHPP), (2) design criteria for partitioning participants based on their interactions with the HealthierU website, (3) evaluate the program impact by comparing behavior changes across different groups of participants. Our case study shows that during the program the participants significantly increased their purchases of some fresh products. Both the distribution of behavior patterns and impact scores show that the program imposed relatively strong impact on the participants who logged activities and tracked weights. Our method can facilitate the enhancement of personalized health programs, especially aiming to maximize the program impact and targeting participants through web or mobile applications

Enhancing Video Recommendation Using Multimedia Content

Video recordings are complex media types. When we watch a movie, we can effortlessly register a lot of details conveyed to us (by the author) through different multimedia channels, in particular, the audio and visual modalities. To date, majority of movie recommender systems use collaborative filtering (CF) models or content-based filtering (CBF) relying on metadata (e.g., editorial such as genre or wisdom of the crowd such as user-generated tags) at their core since they are human-generated and are assumed to cover the 'content semantics' of movies by a great degree. The information obtained from multimedia content and learning from muli-modal sources (e.g., audio, visual and metadata) on the other hand, offers the possibility of uncovering relationships between modalities and obtaining an in-depth understanding of natural phenomena occurring in a video. These discerning characteristics of heterogeneous feature sets meet users' differing information needs. In the context of this Ph.D. thesis [9], which is briefly summarized in the current extended abstract, approaches to automated extraction of multimedia information from videos and their integration with video recommender systems have been elaborated, implemented, and analyzed. Variety of tasks related to movie recommendation using multimedia content have been studied. The results of this thesis can motivate the fact that recommender system research can benefit from knowledge in multimedia signal processing and machine learning established over the last decades for solving various recommendation tasks

Maximum Length Weighted Nearest Neighbor Approach for Electricity Load Forecasting

In this paper we present a new approach for time series forecasting, called Maximum Length Weighted Nearest Neighbor (MLWNN), which combines prediction based on sequence similarity with optimization techniques. MLWNN predicts the 24 hourly electricity loads for the next day, from a time sequence of previously electricity loads up to the current day. We evaluate MLWNN using electricity load data for two years, for three countries (Australia, Portugal and Spain), and compare its performance with three state-of-the-art methods (weighted nearest neighbor, pattern sequence-based forecasting and iterative neural network) and with two baselines. The results show that MLWNN is a promising approach for one day ahead electricity load forecasting

Dynamically identifying relevant EEG channels by utilizing channels classification behaviour

© 2017 Elsevier Ltd It is well established that multiple EEG channels are required for various brain functionality studies, including classification tasks. Yet, due to the curse of dimensionality problem, the analysis of multiple channels may not lead to the desired performance. Accordingly, a number of static channel selection algorithms have been proposed to identify the most relevant subset of channels. However, static methods select a fixed subset of channels that is unchanged when processing new data, and hence cannot adapt to changes in data. In this paper, we propose a novel algorithm that utilizes the dynamic classification behaviour of channels in selecting the channel that is most relevant for each time segment of the signal. The main idea is to identify for each time segment of every channel of the signal (testing sample) the closest training samples. These training samples are used to estimate the local accuracy of each channel. The best performing channel for that time segment will then be identified as the relevant one. Results obtained using EEG data of a four-class alertness state classification problem, with two different feature sets, reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. More importantly, the evaluation of the selected channels reveals that our approach is able to select relevant channels for each of the four alertness states. The proposed algorithm is expected to make a valuable contribution to the field of multichannel biomedical signal classification

DAAR: A discrimination-aware association rule classifier for decision support

© Springer-Verlag GmbH Germany 2017. Undesirable correlations between sensitive attributes (such as race, gender or personal status) and the class label (such as recruitment decision and approval of credit card), may lead to biased decision in data analytics. In this paper, we investigate how to build discrimination-aware models even when the available training set is intrinsically discriminating based on the sensitive attributes. We propose a new classification method called Discrimination-Aware Association Rule classifier (DAAR), which integrates a new discrimination-aware measure and an association rule mining algorithm. We evaluate the performance of DAAR on three real datasets from different domains and compare DAAR with two non-discrimination-aware classifiers (a standard association rule classification algorithm and the state-of-the-art association rule algorithm SPARCCC), and also with a recently proposed discrimination-aware decision tree method. Our comprehensive evaluation is based on three measures: predictive accuracy, discrimination score and inclusion score. The results show that DAAR is able to effectively filter out the discriminatory rules and decrease the discrimination severity on all datasets with insignificant impact on the predictive accuracy. We also find that DAAR generates a small set of rules that are easy to understand and applied by users, to help them make discrimination-free decisions