Board coherence in Pinterest

Kamath, Krishna; Popescu, Ana-Maria; Caverlee, James

doi:10.1145/2487788.2487806

Cited by 8 publications

(14 citation statements)

References 2 publications

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“…The second class of social media user profiling focuses on modelling hidden user attributes and social roles as part of building models for applications like social recommender systems and topic models for documents [11,12,13,14,15,16,17]. Modelling users for social recommender systems is based on mining social media contents or social links among users.…”

Section: Related Workmentioning

confidence: 99%

“…Research focuses on mining user generated social media contents like documents, tweets and social tags for the analysis and prediction of specific attributes of users, including location, age, gender and interests [7,8,9,10]. The second class of user profiling research focuses on modelling users as part of building models for applications like social recommender systems [11,12,13,14,15,16,17] and analysis of topics for documents [18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

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MLLDA: Multi-level LDA for modelling users on content curation social networks

Wang

Zhang

et al. 2017

Neurocomputing

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MLLDA: Multi-level LDA for modelling users on content curation social networks

Wang

Zhang

et al. 2017

Neurocomputing

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“…Some work has been done using topic representations to discover the latent themes: [5] studied how training a topic model on Twitter data can improve performance in document classifications, [7] explored topic models for analyzing disaster-related Twitter data, [13] investigated how to improve topic models given the short and messy texts on tweets. Regarding Pinterest data, some work has been done to perform board recommendations [6], and implementations of topic models to understand users' interests [15]. However, there is little work regarding product recommendations in this setting.…”

Section: Related Workmentioning

confidence: 99%

“…For our sample dataset, the average number of pins per user is 2,476, while the average number of pins per board is 55. 6.…”

Section: Dataset I: Pinterestmentioning

confidence: 99%

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Are words enough?

Zoghbi

Vulić

Moens

2013

Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing

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User-generated content offers opportunities to learn about people's interests and hobbies. We can leverage this information to help users find interesting shops and businesses find interested users. However this content is highly noisy and unstructured as posted on social media sites and blogs. In this work we evaluate different textual representations and retrieval models that aim to make sense of social media data for retail applications. Our task is to link the text of pins (from Pinterest.com) to online shops (formed by clustering Amazon.com's products). Our results show that document representations that combine latent concepts with single words yield the best performance.

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Collecting, organizing, and sharing pins in pinterest

Han

Choi

Chun

et al. 2014

The 2014 ACM International Conference on Measurement and Modeling of Computer Systems

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Pinterest, a popular social curating service where people collect, organize, and share content (pins in Pinterest), has gained great attention in recent years. Despite the increasing interest in Pinterest, little research has paid attention to how people collect, manage, and share pins in Pinterest. In this paper, to shed insight on such issues, we study the following questions. How do people collect and manage pins by their tastes in Pinterest? What factors do mainly drive people to share their pins in Pinterest? How do the characteristics of users (e.g., gender, popularity, country) or properties of pins (e.g., category, topic) play roles in propagating pins in Pinterest? To answer these questions, we have conducted a measurement study on patterns of pin curating and sharing in Pinterest. By keeping track of all the newly posted and shared pins in each category (e.g., animal, kids, women's fashion) from June 5 to July 18, 2013, we built 350 K pin propagation trees for 3 M users. With the dataset, we investigate: (1) how users collect and curate pins, (2) how users share their pins and why, and (3) how users are related by shared pins of interest. Our key finding is that pin propagation in Pinterest is mostly driven by pin's properties like its topic, not by user's characteristics like her number of followers. We further show that users in the same community in the interest graph (i.e., representing the relations among users) of Pinterest share pins (i) in the same category with 94% probability and (ii) of the same URL where pins come from with 89% probability. Finally, we explore the implications of our findings for predicting how pins are shared in Pinterest.

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Board coherence in Pinterest

Cited by 8 publications

References 2 publications

MLLDA: Multi-level LDA for modelling users on content curation social networks

MLLDA: Multi-level LDA for modelling users on content curation social networks

Are words enough?

Collecting, organizing, and sharing pins in pinterest

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