Recommending Flickr groups with social topic model

Wang, Jingdong; Zhao, Zhe; Zhou, Jiazhen; Wang, Hao; Cui, Bin; Qi, Guo-Jun

doi:10.1007/s10791-012-9193-0

Cited by 56 publications

(19 citation statements)

References 32 publications

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Section: A Range Of Definitions Of Social Recommendationsmentioning

confidence: 99%

“…Depending on how one machine is trustworthy to another machine, the researchers interpreted machine-to-machine connections as a trust-based network, and computed how the trust values are inferred and propagated. [3,131].In summary, we limit the main body of this chapter to the studies satisfying four criteria -1) they should suggest items of interests; 2) the target recipients of their recommendations should be individual users; 3) the personalization should take into account the opinions of users' social networks; and 4) users should explicitly define their social networks. …”

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confidence: 99%

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Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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Abstract. The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward. IntroductionThe remarkable popularity of social media encourages web users to participate in various online activities and to generate data on an unprecedented scale. Given exponentially growing social media data volumes, personalized recommendation technologies have proven the effectiveness as a solution of the information overload problem; have offered positive user experiences with more relevant contents; and have presented competitive advantage to the social media business [119]. As predictive analysis of online users' tastes, recommendation technology adaptively filters out unnecessary data and selectively chooses presumably favorable information according to users' preferences. At odds with the original principle of personalized recommendations, which is to give users control of information access, however, the typical and most popular collaborative filtering recommendation technology performs all steps autonomously and allows no room for users to get involved in information personalization of their own. The lacking user involvement causes several problems (illustrated in Section 12.3.1), and many researchers have called attention to solutions to improve the quality of typical recommendation technology and to cope with the relevant problems. Among the several streams of research to develop the solutions, one important evolution is to take advantage of users' self-defined online social networks and proposes to generate recommendations based on users' social linkssocial link-based recommendations. The idea to utilize users' online social links as a foundation of recommendation is undoubtedly spurred by the recent phenomenon of online social networks.Compared with the era when web users stayed in isolation, a number of social media systems have been adapting online sociability, helping users to find people of interest, and as well as encouraging them to socially associate with people of interest. In fact, web users' active and eager participation on social media are intrinsically motivated by not only personal desire for information management and knowledge acquisition but also social desire for engagement and communication with...

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Section: A Range Of Definitions Of Social Recommendationsmentioning

confidence: 99%

mentioning

confidence: 99%

Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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“…Jaffe et al [60] generated summaries by selecting the most representative photos, using geo-tags and a clustering approach. Wang et al [61] proposed a generative probabilistic model and used it for group recommendation. A similar work by Chen et al [62], focused on the creation of visual summaries to be used as tourist maps, by capturing the most important points of interest.…”

Section: Other Application Domainsmentioning

confidence: 99%

An Overview of Flickr Challenges and Research Opportunities

Spyrou

Mylonas

2014

2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization

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Abstract-It is rather true that nowadays diversity and a potential for growth characterize the field of popular social networks like Flickr. Based on this observation, we provide herein a brief overview of the basic characteristics, challenges and research opportunities of the latter. Some historical facts of its origin are presented, followed by a review of its digital photo sharing and tagging capabilities, focusing on the most popular related research fields, like travel applications, knowledge extraction and human activity tracking. Throughout the paper the status of current research and directions for future research are highlighted in an effort to provide useful information to fellow researchers.

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“…A typical resource for this purpose is Flickr, the most famous image sharing platform. Images taken from Flickr have been broadly utilized in various researches such as preference recommendation [22,18], community detection [13] and learning to rank for images [17]. In particular, Qi et al [14] presented a method of embedding both content similarities and content-tag links in SNSs.…”

Section: Related Workmentioning

confidence: 99%

Image context discovery from socially curated contents

Kimura

Ishiguro²,

Yamada

et al. 2013

Proceedings of the 21st ACM International Conference on Multimedia

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This paper proposes a novel method of discovering a set of image contents sharing a specific context (attributes or implicit meaning) with the help of image collections obtained from social curation platforms. Socially curated contents are promising to analyze various kinds of multimedia information, since they are manually filtered and organized based on specific individual preferences, interests or perspectives. Our proposed method fully exploits the process of social curation: (1) How image contents are manually grouped together by users, and (2) how image contents are distributed in the platform. Our method reveals the fact that image contents with a specific context are naturally grouped together and every image content includes really various contexts that cannot necessarily be verbalized by texts.

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Recommending Flickr groups with social topic model

Cited by 56 publications

References 32 publications

Recommendations Based on Social Links

Recommendations Based on Social Links

An Overview of Flickr Challenges and Research Opportunities

Image context discovery from socially curated contents

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