Cross-Domain Recommendation via Cluster-Level Latent Factor Model

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A Survey on Negative Transfer (2020, background): “One solution is parameter softsharing [167], which re-weights and combines classifiers trained on multiple source domains based on the relationship between the source and target domains.”
Cross-Domain Recommender Systems (2015, methodology): “…music, movies & TV shows ratings Winoto & Tang 2008 [66] item books, movies ratings - BookCrossing, MovieLens/EachMovie Li et al. 2009 [40] [41] Gao et al. 2013 [26] ratings, tags - LibraryThing, MovieLens Zhang et al. 2010 [67] Shi et al. 2011 [59] Enrich et al. 2013 [20] ratings,…”
A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018, methodology): “• Neural network based cross-domain recommendation systems NeuCDCF, CCCFNet and GCBAN perform better than the traditional cross-domain recommendation system CLFM.”
Ieee Transactions on Knowledge and Data Engineering, Manuscropt Id 1 Social Recommendation with Cross-domain Transferable Knowledge (unknown): “Gao et al. [21] conducted recommendation via a cluster-level latent factor model.”
A Deep Framework for Cross-Domain and Cross-System Recommendations (2018, background): “In contrast, Transfer-based Approaches in CDR mainly focus on transferring latent factors [Pan et al. , 2010] or rating patterns [Gao et al. , 2013] from the source domain to the target domain.”