VideoTopic: Content-Based Video Recommendation Using a Topic Model

Zhu, Qiusha; Shyu, Mei‐Ling; Wang, Haohong

doi:10.1109/ism.2013.41

Cited by 50 publications

(23 citation statements)

References 9 publications

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“…Content-based recommendation [31]: Content-based methods analyze the common features among the services a user has already rated highly. Only the services similar to user's past preferences are then recommended; Collaborative recommendation [32]: Collaborative methods recommend services to the user that people with similar tastes and preferences have liked in the past; Hybrid recommendation: It is a combination of collaborative and content-based methods to avoid certain limitations of content-based and collaborative systems.…”

Section: Recommendation Systems and Techniquesmentioning

confidence: 99%

Service selection and recommendation in integrated network environment

Zhang

Yang

Guo

et al. 2017

Network as a Service for Next Generation Internet

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With the rapid development of information technology and network technology, the Internet is becoming more intelligent, personalized, and social, influencing and changing people's way of life. Many heterogeneous networks (mobile network, Internet, Television broadcasting network, etc.) and technology are integrated into an open communication network, that is, the integrated network. In the context of network integration, a large number of services, provided by service providers, have sprung to meet various needs of service consumers or users. Service is becoming a "collective term" that is also known as "everything as a service." Various services, including online shopping, music download, live streaming video, social networking, and various mobile apps, improve the efficiency of people's work and facilitate people's life. The ubiquitous and openness characters of the integrated network provide users with personalized services better. However, along with the explosion of services in the integrated network, there have emerged new problems. On the one hand, to the user, with the rapid growth of the type and number of services, the users run into the trouble of information overload, and usually users need to spend a lot of time finding themselves services they need; on the other hand, to the service provider, the process that users browse a large number of irrelevant services will no doubt make consumers submerged in the problem of information overload in the continuous loss. How to achieve fast and reliable selection and recommendation of optimal services for users in integrated network environment has become one of the most challenging issues in the field of service computing [1]. One of the most typical examples is social networking services. Because social networking service always has a huge user groups and users frequently update status, causing social networking services would produce lots of users and redundant

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Section: Recommendation Systems and Techniquesmentioning

confidence: 99%

Service selection and recommendation in integrated network environment

Zhang

Yang

Guo

et al. 2017

Network as a Service for Next Generation Internet

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“…All these approaches focus on the personalized recommendations, which recommend videos matching to users' profiles or interests, while not the item itself. In [39], a content-based framework VideoTopc was proposed for movie recommendation. VideoTopic exploits both visual features and textual features of videos to construct a topic model, which represents the video content and links to its user interests.…”

Section: Video Recommendationmentioning

confidence: 99%

Online Video Recommendation in Sharing Community

Zhou

Chen

Zhang

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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The creation of sharing communities has resulted in the astonishing increasing of digital videos, and their wide applications in the domains such as entertainment, online news broadcasting etc. The improvement of these applications relies on effective solutions for social user access to video data. This fact has driven the recent research interest in social recommendation in shared communities. Although certain effort has been put into video recommendation in shared communities, the contextual information on social users has not been well exploited for effective recommendation. In this paper, we propose an approach based on the content and social information of videos for the recommendation in sharing communities. Specifically, we first exploit a robust video cuboid signature together with the Earth Mover's Distance to capture the content relevance of videos. Then, we propose to identify the social relevance of clips using the set of users belonging to a video. We fuse the content relevance and social relevance to identify the relevant videos for recommendation. Following that, we propose a novel scheme called sub-community-based approximation together with a hash-based optimization for improving the efficiency of our solution. Finally, we propose an algorithm for efficiently maintaining the social updates in dynamic shared communities. The extensive experiments are conducted to prove the high effectiveness and efficiency of our proposed video recommendation approach.

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“…Much research over the past decade has focused on developing service recommendation systems, and they are usually classified based on recommendation approach as content-based recommendation [10], collaborative filteringbased recommendation [11], and model-based collaborative filtering recommendation [12].…”

Section: Introductionmentioning

confidence: 99%

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Guo

2018

Scientific Programming

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In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

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VideoTopic: Content-Based Video Recommendation Using a Topic Model

Cited by 50 publications

References 9 publications

Service selection and recommendation in integrated network environment

Service selection and recommendation in integrated network environment

Online Video Recommendation in Sharing Community

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

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