Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied

Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices