CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Understanding user experience of mobile video: Framework, measurement, and optimization

Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Smoothness perception : investigation of beat rate effect on frame rate perception

Despite the complexity of the Human Visual System (HVS), research over the last few decades has highlighted a number of its limitations. These limitations can be exploited in computer graphics to significantly reduce computational cost and thus required rendering time, without a viewer perceiving any difference in resultant image quality. Furthermore, cross-modal interaction between different modalities, such as the influence of audio on visual perception, has also been shown as significant both in psychology and computer graphics. In this paper we investigate the effect of beat rate on temporal visual perception, i.e. frame rate perception. For the visual quality and perception evaluation, a series of psychophysical experiments was conducted and the data analysed. The results indicate that beat rates in some cases do affect temporal visual perception and that certain beat rates can be used in order to reduce the amount of rendering required to achieve a perceptual high quality. This is another step towards a comprehensive understanding of auditory-visual cross-modal interaction and could be potentially used in high-fidelity interactive multi-sensory virtual environments

Supporting Personalized Music Exploration through a Genre Exploration Recommender

Recommender systems have been largely focused on the task of predicting users' current preferences and finding the most relevant items that users currently like. However, this approach is not sufficient as users may want to explore and develop new preferences, for example about a new genre. Allowing users to explore new preferences has many advantages, such as helping users to stay away from the so-called ``filter bubbles'', supporting new preference exploration and development, and promoting under-explored niche tastes, in addition to the mainstream preferences. Therefore, in this dissertation, we explore how recommender systems can be leveraged to support users' new preference exploration in the context of music genre exploration. The research takes a multidisciplinary approach in which we explore music recommendation algorithms and interactive exploration interface design for supporting music genre exploration, paired with insights from individual's music preference evolution and theories on decision making (such as digital nudges). For this purpose, we propose a music genre exploration tool and refine the tool over subsequent studies. We evaluate the music genre exploration tool with multiple single-session user-centric studies and one longitudinal user study on the long-term effectiveness of the tool to drive new preference exploration with various types of users’ objective behavior and their subjective user experience. From the studies, we find that users perceived the music genre exploration tool to be a new and helpful way to explore and develop new music tastes. By allowing users to make trade-offs between their current preferences and the new music genre they want to explore, the music genre exploration helps users make an easy personalized first step out of their comfort zone and towards the new preferences. The newly designed interactive exploration interface of the music exploration tool improves the usability and helpfulness of genre exploration by improving transparency, controllability and understandability. We further investigate individual differences during musical preference evolution by checking individuals' musical preference consistency and identify a relevant personal factor associated with this consistency (i.e., musical expertise). Our findings suggest that users with different musical expertise tend to show different musical exploration behavior. We further enhance the exploration tool with digital nudges to see if digital nudges can promote more exploration from users, and based on insights on individual differences, how this differs among individuals with different expertise levels. Based on our findings, we discuss opportunities and implications for future recommender systems to support new preference exploration and development

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research