Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been extensively used to understand socioeconomic and political phenomena and user behaviour, the implicit feedback provided by users on Tweets through their engagements on the Home Timeline has only been explored to a limited extent. At the same time, there is a lack of large-scale public social network datasets that would enable the scientific community to both benchmark and build more powerful and comprehensive models that tailor content to user interests. By releasing an original dataset of 160 million Tweets along with engagement information, Twitter aims to address exactly that. During this release, special attention is drawn on maintaining compliance with existing privacy laws. Apart from user privacy, this paper touches on the key challenges faced by researchers and professionals striving to predict user engagements. It further describes the key aspects of the RecSys 2020 Challenge that was organized by ACM RecSys in partnership with Twitter using this dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSimilar Papers 제목 키워드 기반
Position Paper on Simulating Privacy Dynamics in Recommender Systems
In this position paper, we discuss the merits of simulating privacy dynamics in recommender systems. We study this issue at hand from two perspectives: Firstly, we present a conceptual approach to integrate privacy into …
PositionRecommendation SystemsStronger Privacy for Federated Collaborative Filtering with Implicit Feedback
Recommender systems are commonly trained on centrally collected user interaction data like views or clicks. This practice however raises serious privacy concerns regarding the recommender's collection and handling of pot…
Collaborative FilteringRecommendation SystemsBlockchain-based Recommender Systems: Applications, Challenges and Future Opportunities
Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of…
Recommendation SystemsA Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research
Recommender systems are essential for personalizing digital experiences on e-commerce sites, streaming services, and social media platforms. While these systems are necessary for modern digital interactions, they face fa…
FairnessPrivacy PreservingRecommendation SystemsSurvey for Trust-aware Recommender Systems: A Deep Learning Perspective
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to emb…
Deep LearningRecommendation Systems