Collaborative filtering via gaussian probabilistic latent semantic analysis

By Thomas Hofmann. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003

444 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/12f46619885bdebc60085d4f49f7a90ef2614519

How researchers see this paper

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

In Artificial Intelligence

In Information Systems

In Transportation

Citations in the corpus

SoRec: social recommendation using probabilistic matrix factorization (2008, methodology): “Examples of model-based approaches include the clustering model [25], aspect models [8, 9, 20] and the latent factor model [3].”
Social Recommendation Using Low-Rank Semidefinite Program (2011, methodology): “On the other hand, the model-based approaches (Hofmann 2003; Zhang and Koren 2007; Si and Jin 2003) aim to take advantage of the statistical and machine learning techniques to train a compact model from the rating data.”
Recommender systems with social regularization (2011, methodology): “Algorithms in this category include the clustering model [13], the aspect models [8, 9, 33], the latent factor model [5], the Bayesian hierarchical model [40] and the ranking model [19].”
Travel route recommendation using geotags in photo sharing sites (2010, background): “Topic models have been successfully used in a wide variety of applications such as information retrieval and language modeling [22, 21] as well as modeling user interests [5, 6].”
Cold-start Active Learning with Robust Ordinal Matrix Factorization (2014, methodology): “…computational cost they approximate (5) or perform approximate updates with simple models where updates are fast, such as multiple-cause vector quantizations (Ross & Zemel, 2002), naive Bayes (Boutilier et al., 2002), the aspect model (Hofmann, 2003) and ﬂexible mixture models (Si & Jin, 2003).”
Personalized entity recommendation: a heterogeneous information network approach (2014, background): “As one of the most popular recommendation approaches, collaborative filtering techniques have been extensively studied from different perspectives[23] [8].”
Recommender Systems (2012, methodology): “As the above pLSA model considers a multinomial distribution φ ( k ) α which can be used to model only binary preferences, one may consider the generalized pLSA [193] which allows for numeric ratings.”
ArnetMiner: extraction and mining of academic social networks (2008, methodology): “Recently, probabilistic topic models such as probabilistic Latent Semantic Indexing (pLSI) [17], Latent Dirichlet Allocation (LDA) [8], and Author-Topic model [25] [26] have been proposed as well as successfully applied to multiple text mining tasks such as information retrieval [29], collaborative ”
Personalized active learning for collaborative filtering (2008, methodology): “We use a multinomial model for the user-group mixing proportions P (z|u), as proposed in [2].”
Fashion Coordinates Recommender System Using Photographs from Fashion Magazines (2011, unknown): “Topic models are successfully used for a wide variety of applications including information retrieval [ Blei et al. , 2003; Hofmann, 1999], collaborative ﬁltering [Hofmann, 2003; Iwata et al. , 2009a], and modeling images [Blei and Jordan, 2003; Cao and Fei-Fei, 2007; Iwata et al. , 2009b].”
Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model (2018, methodology): “Thomas Hofmann [6] and Rong Jin [7] take the user’s rating trend into account and translate the ratings into normalized intervals by employing Gaussian and Decoupling normalization.”
Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences (2005, methodology): “Examples include statistical techniques such as Principle Component Analysis (PCA) [7] and information retrieval techniques such as Latent Semantic Indexing (LSI) [8, 9, 10].”
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].”
Collaborative Filtering: A Machine Learning Perspective (2004, unknown): “Hofmann has recently carried out this exercise with the triadic aspect model [30].”