Latent dirichlet allocation for tag recommendation

520 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016, background): “First, tags are often assigned by users, which may lead to a cold-start problem [34], since new items have no annotation.”
Recommender Systems (2016, unknown): “Probabilistic latent factor models are presented in [316].”
Semantic Matching in Search (2014, methodology): “[105] try to annotate tags to images using LDA.”
SemRec: A Semantic Enhancement Framework for Tag Based Recommendation (2011, methodology): “However via latent semantic analysis, we can further notice that the tags of “Howto, Guide, Tips and Tutorial” actually form a semantic topic of “Howto” in hidden topic space (Krestel, Fankhauser, and Nejdl 2009).”
Challenging the Long Tail Recommendation (2012, methodology): “[17] uses an approach based on LDA for recommending tags.”
A location-based news article recommendation with explicit localized semantic analysis (2013, background): “Word frequencies or topics are the most widely used representation of articles [11, 18], but recently, topic representation is preferred because a topic is a good proxy for article contents [23].”
Recommender Systems (2012, background): “4) to extract latent topics from the available tags in the user or object space and then produce recommendations using classical probabilitybased models [276, 277, 278].”