Comparative Analysis of Functionality and Aspects for Hybrid Recommender Systems

Recommender systems are gradually becoming the backbone of profitable business which interact with users mainly on the web stack. These systems are privileged to have large amounts of user interaction data used to improve them.  The systems utilize machine learning and data mining techniques to determine products and features to suggest different users correctly. This is an essential function since offering the right product at the right time might result in increased revenue. This paper gives focus on the importance of different kinds of hybrid recommenders. First, by explaining the various types of recommenders in use, then showing the need for hybrid systems and the multiple kinds before giving a comparative analysis of each of these. Keeping in mind that content-based, as well as collaborative filtering systems, are widely used, research is comparatively done with a keen interest on how this measures up to hybrid recommender systems

pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems

Nearest neighbor (NN) sampling provides more semantic variations than pre-defined transformations for self-supervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality of the support set, which holds positive samples for the contrastive loss. In this work, we show that the quality of the support set plays a crucial role in any nearest neighbor based method for SSL. We then provide a refined baseline (pNNCLR) to the nearest neighbor based SSL approach (NNCLR). To this end, we introduce pseudo nearest neighbors (pNN) to control the quality of the support set, wherein, rather than sampling the nearest neighbors, we sample in the vicinity of hard nearest neighbors by varying the magnitude of the resultant vector and employing a stochastic sampling strategy to improve the performance. Additionally, to stabilize the effects of uncertainty in NN-based learning, we employ a smooth-weight-update approach for training the proposed network. Evaluation of the proposed method on multiple public image recognition and medical image recognition datasets shows that it performs up to 8 percent better than the baseline nearest neighbor method, and is comparable to other previously proposed SSL methods.Comment: 15 pages, 5 figure

Towards a smart smoking cessation app: a 1D-CNN model predicting smoking events

Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app

Using Deep Learning Predictions of Smokers’ Behaviour to Develop a Smart Smoking-Cessation App

The number of new smoking-cessation apps had increased in recent years. Although these offer accessible and low-cost support to smokers, they often lack scientific understanding of nicotine addiction, and rely on smokers’ self-reporting their cravings / environmental factors; a method widely acknowledged to be unreliable. This PhD presents two novel deep-learning models for automatic smoking events prediction. Both models combine machine-learning with Control Theory Model of Smoking (CTMoS), to enable the prediction of smoking events based on both internal (nicotine level) and external (e.g. location) factors. This offers a way to overcome limitations of previous apps. The first model, combined CTMoS with a 1D Convolutional Neural Network, using raw accelerometer and GPS coordinates as input. Result indicated good prediction of internal craving factors (e.g. nicotine level and craving); but smoking events prediction required improvement, as the f1-score were 0.06, 0.14, 0.24, and 0.4 for predicting a smoking event 5, 15, 30, and 60 -min (respectively) prior to its occurrence. The second model combined 1D Convolutional Neural Network with the Bidirectional Long Short-Term Memory method, to create a deep learning model with Genetic Algorithm for hyperparameter selection. The model used the same 3- accelerometer values as input, but the 3-GPS coordinates were replaced with coded location data (five most smoked locations). These changes improved smoking events prediction with average f1-score of 0.32, 0.59, 0.71, and 0.8 for predicting a smoking event 5, 15, 30, and 60 -min (respectively) prior to its occurrence. This PhD achieved its three goals: minimize user input (by using data collected from phone sensors); improve scientific understanding of factors that influence smokers’ behaviour (by evaluating the relative contribution of different factors), and developing a state-of-the-art model that enables the automatic prediction of smoking events. As such, outcomes of this PhD lay the foundation for future development of smart and personalized apps that can provide real-time personalized support for smokers