Mining Cross-network Association for YouTube Video Promotion

Yan, Ming; Sang, Jitao; Xu, Changsheng

doi:10.1145/2647868.2654920

Cited by 43 publications

(15 citation statements)

References 28 publications

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“…When we look through existing multi-modal models in terms of the association knowledge, one group of methods follow a user-centric way, which focuses on cross-modal information of overlapped users. A straight forward solution is to treat cross-modal association as a linear transfer problem, and pursue an explicit transfer matrix based on regression [156]- [158]. The objective function for this type of models can be expressed as follows:…”

Section: )mentioning

confidence: 99%

Multi-Modal Deep Analysis for Multimedia

Zhu

Wang

Zhang

2020

IEEE Trans. Circuits Syst. Video Technol.

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With the rapid development of Internet and multimedia services in the past decade, a huge amount of usergenerated and service provider-generated multimedia data become available. These data are heterogeneous and multi-modal in nature, imposing great challenges for processing and analyzing them. Multi-modal data consist of a mixture of various types of data from different modalities such as texts, images, videos, audios etc. In this article, we present a deep and comprehensive overview for multi-modal analysis in multimedia. We introduce two scientific research problems, data-driven correlational representation and knowledge-guided fusion for multimedia analysis. To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2)multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge. More specifically, on datadriven correlational representation, we highlight three important categories of methods, such as multi-modal deep representation, multi-modal transfer learning, and multi-modal hashing. On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question answering, multi-modal video summarization, multimodal visual pattern mining and multi-modal recommendation. Finally, we bring forward our insights and future research directions.

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Section: )mentioning

confidence: 99%

Multi-Modal Deep Analysis for Multimedia

Zhu

Wang

Zhang

2020

IEEE Trans. Circuits Syst. Video Technol.

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“…Immersive recommendation is a generalization of the prior work on cross-platform recommendation, where user data in one platform is used to improve recommendations on another platform. For example, prior work used social media records to recommend Pin-terest boards [49], Youtube videos [46], and ebooks [39], or aggregated the user profile across different platforms to streamline the on-boarding process on a new platform [3]. However, whereas most prior studies focused on using specific data sources to improve the cold-start recommendations for a specific application, in immersive recommendation, we developed techniques that are able to simultaneously profile multi-context data and improve recommendations in multiple applications beyond the cold-start phase and throughout a user's lifetime as the interests change.…”

Section: Related Workmentioning

confidence: 99%

Immersive Recommendation

Hsieh

Yang

Wei

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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We propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our contextaware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work we target personalized news and local event recommendations for their utility and societal importance. We evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition, we conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model showed significant improvement over the stateof-the-art algorithms, suggesting the value of using this new usercentric recommendation model to improve recommendation quality, including in cold-start situations.

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“…Cross network analysis aims to merge social signals from different network to increase online social media platform engagement. For example, in [10], Yan et al proposed to identify the best Twitter accounts to promote YouTube videos, by mining the associations between topics learning from user tweets and their favorite YouTube videos. User modeling is the foundation of personalized services, such as personalized recommendation, search engine reranking and advertisements targeting.…”

Section: Related Workmentioning

confidence: 99%

Pinterest Board Recommendation for Twitter Users

Yang

Luo

2015

Proceedings of the 23rd ACM International Conference on Multimedia

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Pinboard on Pinterest is an emerging media to engage online social media users, on which users post online images for specific topics. Regardless of its significance, there is little previous work specifically to facilitate information discovery based on pinboards. This paper proposes a novel pinboard recommendation system for Twitter users. In order to associate contents from the two social media platforms, we propose to use MultiLabel classification to map Twitter user followees to pinboard topics and visual diversification to recommend pinboards given user interested topics. A preliminary experiment on a dataset with 2000 users validated our proposed system

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Mining Cross-network Association for YouTube Video Promotion

Cited by 43 publications

References 28 publications

Multi-Modal Deep Analysis for Multimedia

Multi-Modal Deep Analysis for Multimedia

Immersive Recommendation

Pinterest Board Recommendation for Twitter Users

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