A cross-platform recommendation system from Facebook to Instagram

Chang, Chia-Ling; Chen, Yen-Liang; Li, Jia-Shin

doi:10.1108/el-09-2022-0210

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Also, it can help to characterize more comprehensive user portraits to recognize criminals and identify fake or illegal accounts. In addition, it can also be helpful in cross-platform recommendation systems [3], in constructing user credit systems and risk assessment systems in the financial area, etc.…”

Section: Introductionmentioning

confidence: 99%

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Xie,

Zang,

et al. 2024

Electronics

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Random-walk-based graph representation methods have been widely applied in User Identity Linkage (UIL) tasks, which links overlapping users between two different social networks. It can help us to obtain more comprehensive portraits of criminals, which is helpful for improving cyberspace governance. Yet, random walk generates a large number of repeating sequences, causing unnecessary computation and storage overhead. This paper proposes a novel method called Edge-Removing Walk (ERW) that can replace random walk in random-walk-based models. It removes edges once they are walked in a walk round to capture the l−hop features without repetition, and it walks the whole graph for several rounds to capture the different kinds of paths starting from a specific node. Experiments proved that ERW can exponentially improve the efficiency for random-walk-based UIL models, even maintaining better performance. We finally generalize ERW into a general User Identity Linkage framework called ERW-UIL and verify its performance.

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Section: Introductionmentioning

confidence: 99%

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Xie,

Zang,

et al. 2024

Electronics

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To Explain or Not To Explain: An Empirical Investigation of AI-based Recommendations on Social Media Platforms

Haque,

Islam,

Mikalef

2024

Electron Markets

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Artificial intelligence integration into social media recommendations has significant promise for enhancing user experience. Frequently, however, suggestions fail to align with users’ preferences and result in unfavorable encounters. Furthermore, the lack of transparency in the social media recommendation system gives rise to concerns regarding its impartiality, comprehensibility, and interpretability. This study explores social media content recommendation from the perspective of end users. To facilitate our analysis, we conducted an exploratory investigation involving users of Facebook, a widely used social networking platform. We asked participants about the comprehensibility and explainability of suggestions for social media content. Our analysis shows that users mostly want explanations when encountering unfamiliar content and wish to be informed about their data privacy and security. Furthermore, users favor concise, non-technical, categorical representations of explanations along with the facility of controlled information flow. We observed that explanations impact users’ perception of the social media platform’s transparency, trust, and understandability. In this work, we have outlined design implications related to explainability and presented a synthesized framework of how various explanation attributes impact user experience. In addition, we proposed another synthesized framework for end user inclusion in designing an explainable interactive user interface.

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Content Analysis of Libraries’ Instagram Posts: Cultural Collection, Activities, and Preservation of Cultural Heritage

Rachman,

Gietty Tambunan,

Abdullah Sani

et al. 2023

Preservation, Digital Technology &Amp; Culture

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Libraries’ roles and contributions in promoting and raising awareness of culture and cultural heritage to support the sustainability of cultural life can be strengthened by utilizing social media platforms, including Instagram. However, there is a gap in studies and research relating to how academic libraries reflect their cultural functions through social media, i.e., Instagram. This paper provides a content analysis of academic libraries’ Instagram accounts at three academic libraries located in the United States, Canada, and the United Kingdom. These libraries represent their universities’ concerns with promoting sustainable development goals, specifically Sustainable Development Goal 11 (make cities and human settlements inclusive, safe, resilient, and sustainable). This study analyzed and categorized the Instagram posts of academic libraries related to culture and cultural heritage to answer the following research question: how do academic libraries reflect their cultural functions through social media, i.e., Instagram? The results show that the academic libraries studied here considered reflecting their cultural functions through social media by informing users about various cultural events, collections, facts, and news on Instagram.

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A cross-platform recommendation system from Facebook to Instagram

Cited by 3 publications

References 38 publications

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

To Explain or Not To Explain: An Empirical Investigation of AI-based Recommendations on Social Media Platforms

Content Analysis of Libraries’ Instagram Posts: Cultural Collection, Activities, and Preservation of Cultural Heritage

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