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20172024
most citedSigned Distance-based Deep Memory Recommender

28 citations · 81 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.IR2020

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

Nguyen Vo, Kyumin Lee

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but…

cs.IR202015 cited

Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation

Thanh Tran, Di You, Kyumin Lee

Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representat…

cs.IR2020

Attributed Multi-Relational Attention Network for Fact-checking URL Recommendation

Di You, Nguyen Vo, Kyumin Lee +1

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake…

cs.IR20191 cited

Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist Continuation

Thanh Tran, Renee Sweeney, Kyumin Lee

In this paper, we aim to solve the automatic playlist continuation (APC) problem by modeling complex interactions among users, playlists, and songs using only their interaction dat…

cs.IR201928 cited

Signed Distance-based Deep Memory Recommender

Thanh Tran, Xinyue Liu, Kyumin Lee +1

Personalized recommendation algorithms learn a user's preference for an item by measuring a distance/similarity between them. However, some of the existing recommendation models (e…

cs.IR2018

Regularizing Matrix Factorization with User and Item Embeddings for Recommendation

Thanh Tran, Kyumin Lee, Yiming Liao +1

Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation m…