S
Suvash Sedhain
Researcher at Twitter
Publications - 10
Citations - 1290
Suvash Sedhain is an academic researcher from Twitter. The author has contributed to research in topics: Collaborative filtering & Recommender system. The author has an hindex of 6, co-authored 10 publications receiving 921 citations. Previous affiliations of Suvash Sedhain include NICTA & Australian National University.
Papers
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Proceedings ArticleDOI
AutoRec: Autoencoders Meet Collaborative Filtering
TL;DR: Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.
Proceedings ArticleDOI
Social collaborative filtering for cold-start recommendations
TL;DR: This work formalizes neighborhood-based methods for cold-start collaborative filtering in a generalized matrix algebra framework that does not require purchase data for target users when their side information is available.
Proceedings Article
Low-Rank Linear Cold-Start Recommendation from Social Data.
TL;DR: LoCo is proposed, a new model for cold-start recommendation based on three ingredients: linear regression to learn an optimal weighting of social signals for preferences, a low-rank parametrisation of the weights to overcome the high dimensionality common in social data, and scalable learning of such low- rank weights using randomised SVD.
Proceedings Article
On the effectiveness of linear models for one-class collaborative filtering
TL;DR: LRec is proposed, a variant of SLIM that overcomes limitations without sacrificing any ofSLIM's strengths and consistently and significantly outperforms all existing methods on a range of datasets.
Proceedings Article
Practical linear models for large-scale one-class collaborative filtering
Suvash Sedhain,Hung Bui,Jaya Kawale,Nikos Vlassis,Branislav Kveton,Aditya Krishna Menon,Trung Bui,Scott Sanner +7 more
TL;DR: It is shown that it is possible to scale up linear recommenders to big data by learning an OCCF model in a randomized low-dimensional embedding of the user-item interaction matrix.