Social data: Biases, methodological pitfalls, and ethical boundaries
Social data in digital form—including user-generated content, expressed or implicit relations
between people, and behavioral traces—are at the core of popular applications and …
between people, and behavioral traces—are at the core of popular applications and …
Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges
Over the past two decades, a large amount of research effort has been devoted to
developing algorithms that generate recommendations. The resulting research progress has …
developing algorithms that generate recommendations. The resulting research progress has …
Transformer hawkes process
Modern data acquisition routinely produce massive amounts of event sequence data in
various domains, such as social media, healthcare, and financial markets. These data often …
various domains, such as social media, healthcare, and financial markets. These data often …
node2vec: Scalable feature learning for networks
A Grover, J Leskovec - Proceedings of the 22nd ACM SIGKDD …, 2016 - dl.acm.org
Prediction tasks over nodes and edges in networks require careful effort in engineering
features used by learning algorithms. Recent research in the broader field of representation …
features used by learning algorithms. Recent research in the broader field of representation …
A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
Link prediction in social networks: the state-of-the-art
In social networks, link prediction predicts missing links in current networks and new or
dissolution links in future networks, is important for mining and analyzing the evolution of …
dissolution links in future networks, is important for mining and analyzing the evolution of …
Recommender systems
The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …
recommender systems for filtering the abundant information. Extensive research for …
Social recommendation: a review
Recommender systems play an important role in helping online users find relevant
information by suggesting information of potential interest to them. Due to the potential value …
information by suggesting information of potential interest to them. Due to the potential value …
Leveraging social connections to improve personalized ranking for collaborative filtering
Recommending products to users means estimating their preferences for certain items over
others. This can be cast either as a problem of estimating the rating that each user will give …
others. This can be cast either as a problem of estimating the rating that each user will give …
Meta-prod2vec: Product embeddings using side-information for recommendation
We propose Meta-Prod2vec, a novel method to compute item similarities for
recommendation that leverages existing item metadata. Such scenarios are frequently …
recommendation that leverages existing item metadata. Such scenarios are frequently …