Extending the Bayesian classifier to a context-aware recommender system for mobile devices

Mobile devices that are capable of playing Internet videos have become wide-spread in recent years. Because of the enormous offer of video content, the lack of sufficient presentation space on the screen, and the laborious navigation on mobile devices, the video consumption process becomes more complicated for the end-user. To handle this problem, people need new instruments to assist with the hunting, filtering and selection process. We developed a methodology for mobile devices that makes the huge content sources more manageable by creating a user profile and personalizing the offer. This paper reports the structure of the user profile, the user interaction mechanism, and the recommendation algorithm, an improved version of the Bayesian classifier that incorporates aspects of the consumption context (like time, location, and mood of the user) to make the suggestions more accurate

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

CHORUS Deliverable 4.5: Report of the 3rd CHORUS Conference

The third and last CHORUS conference on Multimedia Search Engines took place from the 26th to the 27th of May 2009 in Brussels, Belgium. About 100 participants from 15 European countries, the US, Japan and Australia learned about the latest developments in the domain. An exhibition of 13 stands presented 16 research projects currently ongoing around the world

Value Creation in a QoE Environment

User behavior of multimedia services currently undergoes strong changes. This is reflected in several recent trends, e.g. the increase of rich media content consumption, preferences for more individual and personalized services and the higher sensitivity of end users for quality issues. These changes will eventually lead to strong changes in network traffic characteristics: rising congestion in peak times and less availability of bandwidth for the individual user. As a result, the quality as perceived by the end-user will decrease if network operators and service providers do not anticipate the required changes for the network. Measurable network requirements such as available video and speech quality, security and reliability are addressed by technologies that are commonly summed up in the Quality of Service (QoS) concept. However, the end-users' perception of quality is only reflected in the wider concept of Quality of Experience (QoE). This takes the measurable network requirements into account as well as customer needs, wants and preferences. For the implementation of QoE technologies several network components need to be added or changed resulting in high capital expenditures. Yet, it is not clear if these costs can be compensated with efficiency increases. Thus, new revenue streams for the network operator are necessary to incentivize investments in QoE technologies. In this paper we address four new value creation models that can serve as basis for more elaborated business models for network operators and other actors. We show how interest in QoE of the user, the content provider, the service provider and the advertiser induces new revenue streams. These models are embedded in five possible future QoE scenarios that reveal regulation, end user quality sensibility and end-to-end support as major issues for the future. --Business Models,Quality of Experience (QoE),Quality of Service (QoS),Value Creation

A dynamic multi-algorithm collaborative-filtering system

Nowadays users have access to an immense number of media content. They are able to consume thousands of Television (TV) channels and millions of video clips from online portals like YouTube. Due to the immense number of available content, users can have the problem to find content of interest. This problem can be solved by recommendation systems. For example, recommendation systems can be used to create recommendations which fit to the preferences of users. Recommendation systems can use two different approaches for the creation of recommendations. They can take content-based and/or collaborative-filtering techniques into account. Content-based filtering techniques use information, the so-called metadata, that describe the content in more detail. Collaborative-filtering techniques calculate similarities e.g., between users. All users are included in a dataset, the so-called community. Generally the number of user profiles within the community is quite large. Examples of such huge communities are Amazon, Netflix, MovieLens, and LastFM. The community which includes the user profiles is used to create a user-item matrix. This user-item matrix contains the preferences from users on items e.g., movies, genres, book titles, and so forth. The quality of the recommendations depends on the accuracy of the predictions. As mentioned above, collaborative-filtering techniques calculate similarities e.g., between users. These similarities can be used to calculate predictions for an entry within the user-item matrix. If the predictions are close or equal to the preferences of a user, the used collaborative-filtering technique predicts accurately. Generally recommendation systems only use one single collaborative-filtering algorithm for the similarity calculation. The research work of this thesis proves that a dynamic selection of the most accurate filtering algorithm by considering more algorithms is able to increase the accuracy of the predictions significantly. In order to increase the accuracy of predictions, this thesis presents a dynamic multi-algorithm collaborative-filtering system which creates recommendations for video content, such as movies or genres. This system is able to find the most accurate filtering algorithm by considering the k-nearest neighbours. These neighbours are selected by identifying the most similar users or items e.g., movies. Besides the dynamic selection, this thesis presents newly developed collaborative-filtering algorithms which are able to overcome researched weaknesses of state-of-the-art algorithms. The evaluation of the proposed system considers a huge dataset from MovieLens and a small dataset from an undertaken survey. The consideration of a huge and a small dataset shall prove that the system can be used in both cases. The results of this thesis show that the proposed system is able to decrease the error rate significantly compared to existing approaches

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research

Noozy AI Development of a recommender system for video-on-demand platform

Recommender algorithms can guide users in a personalized way to interesting objects in a large space of possible options. The necessity of recommendations is increasing on cultural or entertainment and media industries, where the number of products is continuously increasing. Cultural and media platforms and digital markets are getting heavily benefitted from implementing, maintaining, and improving their recommender system. The process on how they retrieve cultural products like music, books, movies, news and enable easy access to the users can have a structural impact on how markets operate alongside how consuming trends change. The prospect of this project is to engineer a complete recommender system for noozy.tv, a new video-on-demand platform dedicated for the viewers of Grand Est region in France. The aim is to develop a framework maintaining standard and modern software development methodologies and tools to ensure seamless service, research scope on real data and diversity, evaluation, and delivering a platform for further improvement in system.Résumé Les algorithmes de recommandation peuvent guider les utilisateurs de manière personnalisée vers des objets intéressants dans un large espace d'options possibles. La nécessité de recommandations augmente sur les industries culturelles ou du divertissement et des médias, où le nombre de produits ne cesse d'augmenter. Les plateformes culturelles et médiatiques et les marchés numériques bénéficient grandement de la mise en oeuvre, de la maintenance et de l'amélioration de leur système de recommandation. Le processus sur la façon dont ils récupèrent les produits culturels comme la musique, les livres, les films, les actualités et permettent un accès facile aux utilisateurs peut avoir un impact structurel sur le fonctionnement des marchés parallèlement à l'évolution des tendances de consommation. La perspective de ce projet est de concevoir un système de recommandation complet pour noozy.tv, une nouvelle plateforme de vidéo à la demande dédiée aux téléspectateurs de la région Grand Est en France. L'objectif est de développer un cadre maintenant des méthodologies et des outils de développement de logiciels standard et modernes pour assurer un service transparent, une portée de recherche sur les données réelles et la diversité, l'évaluation et la fourniture d'une plate-forme pour une amélioration supplémentaire du système

Into the Black Box: Designing for Transparency in Artificial Intelligence

Indiana University-Purdue University Indianapolis (IUPUI)The rapid infusion of artificial intelligence into everyday technologies means that consumers are likely to interact with intelligent systems that provide suggestions and recommendations on a daily basis in the very near future. While these technologies promise much, current issues in low transparency create high potential to confuse end-users, limiting the market viability of these technologies. While efforts are underway to make machine learning models more transparent, HCI currently lacks an understanding of how these model-generated explanations should best translate into the practicalities of system design. To address this gap, my research took a pragmatic approach to improving system transparency for end-users. Through a series of three studies, I investigated the need and value of transparency to end-users, and explored methods to improve system designs to accomplish greater transparency in intelligent systems offering recommendations. My research resulted in a summarized taxonomy that outlines a variety of motivations for why users ask questions of intelligent systems; useful for considering the type and category of information users might appreciate when interacting with AI-based recommendations. I also developed a categorization of explanation types, known as explanation vectors, that is organized into groups that correspond to user knowledge goals. Explanation vectors provide system designers options for delivering explanations of system processes beyond those of basic explainability. I developed a detailed user typology, which is a four-factor categorization of the predominant attitudes and opinion schemes of everyday users interacting with AI-based recommendations; useful to understand the range of user sentiment towards AI-based recommender features, and possibly useful for tailoring interface design by user type. Lastly, I developed and tested an evaluation method known as the System Transparency Evaluation Method (STEv), which allows for real-world systems and prototypes to be evaluated and improved through a low-cost query method. Results from this dissertation offer concrete direction to interaction designers as to how these results might manifest in the design of interfaces that are more transparent to end users. These studies provide a framework and methodology that is complementary to existing HCI evaluation methods, and lay the groundwork upon which other research into improving system transparency might build