5,180 research outputs found

    On hybrid modular recommendation systems for video streaming

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    The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the tester for assessing the performance of the system. The enabler incorporates a variety of recommendation algorithms that span from collaborative filtering and content-based techniques to ones based on neural networks. It uses the nested cross validation for automatically selecting the best ML algorithm along with its hyper-parameter values for the given input, according to a specific metric. The enabler can be easily extended to include other recommenders and blenders. The enabler has been extensively evaluated in the context of video-streaming. It outperforms various other algorithms, when tested on the Movielens 1M benchmark dataset.encouraging results. Moreover For example, it achieves an RMSE of 0.8206, compared to the state-of-the-art performance of the AutoRec and SVD, 0.827 and 0.845, respectively. A pilot web-based recommendation system was developed and tested in the production environment of a large telecom operator in Greece. Volunteer customers of the video-streaming service provided by the telecom operator employed the system in the context of an out-in-the-wild field study with a post-analysis of the enabler, using the collected ratings of the pilot, demonstrated that it significantly outperforms several popular recommendation algorithms

    Group Modeling : selecting a sequence of television items to suit a group of viewers

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    The Impact of Expert Knowledge on User Behavior in Recommender Systems

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    Using experts in recommender systems can improve the accuracy of recommendations as well as other quality aspects of recommendations. Most studies have tested the impact of expert knowledge in offline tests. However, it is still unclear how user behavior changes when experts are used for recommendation in an online scenario. We therefore deploy a live recommender system based on rules built by employed experts on the video-on-demand platform of a large television network. We find that expert-built rules lead to a similar amount of clip views and platform visits as a standard recommender. However, experts have an influence on the consumed content, focusing users on a few popular categories

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

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    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

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

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    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

    Personalized Recommendation for Balancing Content Generation and Usage on Two-Sided Entertainment Platforms

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    Online entertainment platforms such as Youtube host a vast amount of user-generated content (UGC). The unique feature of two-sided UGC entertainment platforms is that creators’ content generation and users’ content usage can influence each other. However, traditional recommender systems often emphasize content usage but ignore content generation, leading to a misalignment between these two goals. To address the challenge, this paper proposes a prescriptive uplift framework to balance content generation and usage through personalized recommendations. Specifically, we first predict the heterogeneous treatment effects (HTEs) of recommended contents on creators’ content generation and users’ content usage, then consider these two predicted HTEs simultaneously in an optimization model to determine the recommended contents for each user. Using a large-scale real-world dataset, we demonstrate that the proposed recommendation method better balances content generation and usage and brings a 42% increase in participants’ activity compared to existing benchmark methods

    Tune in to your emotions: a robust personalized affective music player

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    The emotional power of music is exploited in a personalized affective music player (AMP) that selects music for mood enhancement. A biosignal approach is used to measure listeners’ personal emotional reactions to their own music as input for affective user models. Regression and kernel density estimation are applied to model the physiological changes the music elicits. Using these models, personalized music selections based on an affective goal state can be made. The AMP was validated in real-world trials over the course of several weeks. Results show that our models can cope with noisy situations and handle large inter-individual differences in the music domain. The AMP augments music listening where its techniques enable automated affect guidance. Our approach provides valuable insights for affective computing and user modeling, for which the AMP is a suitable carrier application

    Current Challenges and Visions in Music Recommender Systems Research

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    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
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