Using facial recognition services as implicit feedback for recommenders

User authentication and feedback gathering are crucial aspects for recommender systems. The most common implementations, a username / password login and star rating systems, require user interaction and a cognitive effort from the user. As a result, users opt to save their password in the interface and optional feedback with a star rating system is often skipped, especially for applica- tions such as video watching in a home environment. In this article, we propose an alternative method for user authentication based on facial recognition and an automatic feedback gathering method by detecting various face characteristics. Using facial recognition with a camera in a tablet, smartphone, or smart TV, the persons in front of the screen can be identified in order to link video watch- ing sessions to their user profile. During video watching, implicit feedback is automatically gathered through emotion recognition, attention measurements, and behavior analysis. An emotion finger- print, which is defined as a unique spectrum of expected emotions for a video scene, is compared to the recognized emotions in order to estimate the experience of a user while watching. An evaluation with a test panel showed that happiness can be most accurately detected and the recognized emotions are correlated with the user’s star rating

Text-based Emotion Aware Recommender

We apply the concept of users' emotion vectors (UVECs) and movies' emotion vectors (MVECs) as building components of Emotion Aware Recommender System. We built a comparative platform that consists of five recommenders based on content-based and collaborative filtering algorithms. We employed a Tweets Affective Classifier to classify movies' emotion profiles through movie overviews. We construct MVECs from the movie emotion profiles. We track users' movie watching history to formulate UVECs by taking the average of all the MVECs from all the movies a user has watched. With the MVECs, we built an Emotion Aware Recommender as one of the comparative platforms' algorithms. We evaluated the top-N recommendation lists generated by these Recommenders and found the top-N list of Emotion Aware Recommender showed serendipity recommendations.Comment: 13 pages, 8 tables, International Conference on Natural Language Computing and AI (NLCAI2020) July25-26, London, United Kingdo

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

An overview of video recommender systems: state-of-the-art and research issues

Video platforms have become indispensable components within a diverse range of applications, serving various purposes in entertainment, e-learning, corporate training, online documentation, and news provision. As the volume and complexity of video content continue to grow, the need for personalized access features becomes an inevitable requirement to ensure efficient content consumption. To address this need, recommender systems have emerged as helpful tools providing personalized video access. By leveraging past user-specific video consumption data and the preferences of similar users, these systems excel in recommending videos that are highly relevant to individual users. This article presents a comprehensive overview of the current state of video recommender systems (VRS), exploring the algorithms used, their applications, and related aspects. In addition to an in-depth analysis of existing approaches, this review also addresses unresolved research challenges within this domain. These unexplored areas offer exciting opportunities for advancements and innovations, aiming to enhance the accuracy and effectiveness of personalized video recommendations. Overall, this article serves as a valuable resource for researchers, practitioners, and stakeholders in the video domain. It offers insights into cutting-edge algorithms, successful applications, and areas that merit further exploration to advance the field of video recommendation

Machine Learning Models for Educational Platforms

Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education. This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com

AndroMedia : Towards a Context-aware Mobile Music Recommender

Portable music players have made it possible to listen to a personal collection of music in almost every situation, and they are often used during some activity to provide a stimulating audio environment. Studies have demonstrated the effects of music on the human body and mind, indicating that selecting music according to situation can, besides making the situation more enjoyable, also make humans perform better. For example, music can boost performance during physical exercises, alleviate stress and positively affect learning. We believe that people intuitively select different types of music for different situations. Based on this hypothesis, we propose a portable music player, AndroMedia, designed to provide personalised music recommendations using the user's current context and listening habits together with other user's situational listening patterns. We have developed a prototype that consists of a central server and a PDA client. The client uses Bluetooth sensors to acquire context information and logs user interaction to infer implicit user feedback. The user interface also allows the user to give explicit feedback. Large user interface elements facilitate touch-based usage in busy environments. The prototype provides the necessary framework for using the collected information together with other user's listening history in a context- enhanced collaborative filtering algorithm to generate context-sensitive recommendations. The current implementation is limited to using traditional collaborative filtering algorithms. We outline the techniques required to create context-aware recommendations and present a survey on mobile context-aware music recommenders found in literature. As opposed to the explored systems, AndroMedia utilises other users' listening habits when suggesting tunes, and does not require any laborious set up processes