A user profile-based personalization system for digital multimedia content

Weiß, Diana; Scheuerer, Johannes; Wenleder, Michael; Erk, Alexander; Gülbahar, Mark; Linnhoff‐Popien, Claudia

doi:10.1145/1413634.1413687

Cited by 16 publications

(13 citation statements)

References 5 publications

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“…This method is accurate but not practical in time-sensitive applications like online VoD systems. Instead, user behaviors, such as the time spent on a page, scrolling and clicks on web pages [36,37], time spent on a video [38,39], and purchases in the past [40], are used as implicit ratings in some applications.…”

Section: Predicting User Interestmentioning

confidence: 99%

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

Tan

Guo

Orgun

et al. 2018

Wireless Communications and Mobile Computing

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With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.

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Section: Predicting User Interestmentioning

confidence: 99%

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

Tan

Guo

Orgun

et al. 2018

Wireless Communications and Mobile Computing

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“…The calculation for the generation of implicit user profile was based on the work of Weis et al [17], which is based on the time a user keeps consuming a specific content (e.g. TV program).…”

Section: ) User Profilementioning

confidence: 99%

A collaborative TV-Internet application model to enrich TV viewing experience in a pervasive way

Ferraz

Silva

2015

2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

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This paper presents an application model for augmenting TV viewing experience. The augmentation consists of additional media resources, which are linked to the Web according to the user profile and to TV metadata. Instead of asking the user for such data, a distributed software system captures them non-intrusively, processes the user and TV program context, and automatically searches for and delivers the context-aware resources to the viewer either on the TV screen or on a second screen. The use of TV metadata as context data is a remarkable feature in this work. Such metadata are carried in the MPEG-2 Transport Stream, which is part of the major digital TV systems in the world. This work deals with problems such as inconsistency of TV metadata, and ineffectiveness of Web search, which could frustrate the viewer's enriched experience. The research indicates that context-aware applications in the television domain should strongly take into account TV metadata captured opportunistically from broadcast streams, in addition to traditional context data, such as location, temperature, device capabilities, among others. The solutions presented in this paper point to a minimum-effort by the TV user, enabling a more useful, easier and more attractive Integrated TV-Internet viewing experience.

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“…Kuniavsky also explains the user profiling approach in the book and he expresses the importance of questionnaires for the profiling process in general. Weiss et al in [18] studied user-profile based personalization in order to select and recommend content with respect to user's interest for automated online video or TV services. Vallet et al in [19] presented personalized multimedia management systems with the capability to filter user preferences on the semantic context of ongoing user activities based on an ontology-driven representation of semantics involved in retrieval actions and preferences.…”

Section: Review Of Literaturementioning

confidence: 99%

System profiles in content-based image indexing and retrieval

Guldogan

Gabbouj

2009

SIViP

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In this paper, a novel study on system profiles and adaptation of parameters for end-users of content-based indexing and retrieval (CBIR) applications are presented. The main objective of the study is improving the overall CBIR application performance in different hardware platforms having different technical capabilities and conditions. We define CBIR system profiles in terms of hardware and system platform attributes and propose CBIR parameters for each profile. Hence, the study consists of two main parts: system profiling and adaptation of indexing and retrieval parameters for each profile. The proposed CBIR parameters are appropriate configurations for optimal CBIR use on every platform. The proposed parameters for each system profile are assessed over a large set of experiments. Experimental studies show that the proposed parameters for each system profile have satisfactory semantic retrieval performance, with reduced computational complexity and storage space requirement. 45 to 78% improvement is achieved in the computational complexity of the retrieval process depending on the profile.Keywords Content-based indexing and retrieval · User hardware system profiles · Indexing and retrieval parameters · Optimization of indexing and retrieval performance

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A user profile-based personalization system for digital multimedia content

Cited by 16 publications

References 5 publications

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

A collaborative TV-Internet application model to enrich TV viewing experience in a pervasive way

System profiles in content-based image indexing and retrieval

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