Modeling User Rating Profiles For Collaborative Filtering

By Benjamin M. Marlin. Neural Information Processing Systems, 2003

348 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/992c958fe0f4bd148b6f4304e1b5e458b8575cb1

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Citations in the corpus

Learning to Rank Features for Recommendation over Multiple Categories (2016, background): “In our datasets, as the rating range is [1,5] and thus the”
Learning from Incomplete Ratings Using Non-negative Matrix Factorization (2006, background): “Generally, they can be divided into two categories: memory based algorithms [1, 6, 16, 17] and model based algorithms [2, 3, 5, 7, 11, 15, 18, 19, 20].”
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure (2015, background): “[17, 18, 19]), but the task is otherwise distinct from ours.”
Pairwise interaction tensor factorization for personalized tag recommendation (2010, methodology): “Also for the related task of item prediction, factorization models are known [20, 6, 16, 17] to outperform models like k-nearest-neighbour collaborative .ltering or the Bayesian models URP [15] and PLSA [4].”
A hierarchical model for ordinal matrix factorization (2011, methodology): “, 2009), those using user rating profiles (Marlin, 2004), restricted Boltzmann machines (Salakhutdinov et al.”
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005, methodology): “Other probabilistic modeling techniques for recommender systems include probabilistic latent semantic analysis [47], [48] and a combination of multinomial mixture and aspect models using generative semantics of Latent Dirichlet Allocation [64].”
A Survey of Collaborative Filtering Techniques (2009, methodology): “A user rating profile (URP) model [97] combines the intuitive appeal of the multinomial mixture model and aspect model [83], with the high-level generative semantics of Latent Dirichlet Allocation (LDA, a generative probabilistic model, in which each item is modeled as a finite mixture over an under”
Strathprints Institutional Repository Bayesian Latent Variable Models for Collaborative Item Rating Prediction. In: Acm Cikm, 2011-03-31. Bayesian Latent Variable Models for Collaborative Item Rating Prediction (background): “His work differs from that of Marlin [13] however by then assuming that there is a rating distribution for each latent interest and item pair.”
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies (2007, background): “(Note that applications in suchdomainshavealreadybeendemonstratedfor.attopicmodels[Pritchardetal.2000;Marlin 2003;Fei-Fei and Perona2005; Blei andJordan2003; Airoldiet al. 2008].)”
Data Mining for Web Personalization (2007, methodology): “Recently, the LDA model has been used in text mining [16], author-topic analysis [131], and collaborative ﬁltering [73].”
Maximum-Margin Matrix Factorization (2004, methodology): “Using a low-rank model for collaborative prediction [5] [6] [3] is straightforward: A lowrank matrix X is sought that minimizes a loss versus the observed entries Y S .”
WSRec: A Collaborative Filtering Based Web Service Recommender System (2009, methodology): “We successfully generate client stub classes for 18,102 Web services using the WSDL2Java tool from the Axis package [1].”
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo (2008, background): “A variety of probabilistic factor-based models have been proposed(Hofmann, 1999; Marlin, 2004; Marlin &Zemel,2004;Salakhutdinov&Mnih,2008).”
Ordinal Boltzmann Machines for Collaborative Filtering (2009, methodology): “, 2007), PLSA (Hofmann, 2004) and LDA (Marlin, 2004) are also competitive.”
Probabilistic Matrix Factorization (2007, background): “A variety of probabilistic factor-based models has been pro posed recently [2, 3, 4].”