Deckard: Scalable and accurate tree-based detection of code clones
Detecting code clones has many software engineering applications. Existing approaches
either do not scale to large code bases or are not robust against minor code modifications. In …
either do not scale to large code bases or are not robust against minor code modifications. In …
Generalized probabilistic matrix factorizations for collaborative filtering
H Shan, A Banerjee - 2010 IEEE international conference on …, 2010 - ieeexplore.ieee.org
Probabilistic matrix factorization (PMF) methods have shown great promise in collaborative
filtering. In this paper, we consider several variants and generalizations of PMF framework …
filtering. In this paper, we consider several variants and generalizations of PMF framework …
[图书][B] Handbook of mixed membership models and their applications
This volume is, in a sense, the culmination of over 20 years of statistical work and over 15
years of personal interactions. One of us, Fienberg, was exposed to the ideas of the Grade of …
years of personal interactions. One of us, Fienberg, was exposed to the ideas of the Grade of …
Human interaction with recommendation systems
S Schmit, C Riquelme - International Conference on Artificial …, 2018 - proceedings.mlr.press
Many recommendation algorithms rely on user data to generate recommendations.
However, these recommendations also affect the data obtained from future users. This work …
However, these recommendations also affect the data obtained from future users. This work …
Model-based collaborative personalized recommendation on signed social rating networks
Recommendation on signed social rating networks is studied through an innovative
approach. Bayesian probabilistic modeling is used to postulate a realistic generative …
approach. Bayesian probabilistic modeling is used to postulate a realistic generative …
Accurate and scalable social recommendation using mixed-membership stochastic block models
With increasing amounts of information available, modeling and predicting user preferences—
for books or articles, for example—are becoming more important. We present a collaborative …
for books or articles, for example—are becoming more important. We present a collaborative …
Bounded matrix factorization for recommender system
Matrix factorization has been widely utilized as a latent factor model for solving the
recommender system problem using collaborative filtering. For a recommender system, all …
recommender system problem using collaborative filtering. For a recommender system, all …
Kernel-mapping recommender system algorithms
Recommender systems apply machine learning techniques for filtering unseen information
and can predict whether a user would like a given item. In this paper, we propose a new …
and can predict whether a user would like a given item. In this paper, we propose a new …
Introduction to Mixed Membership Models and Methods.
Mixed membership models have emerged over the past 20 years as a flexible cluster-like
modeling tool for unsupervised analyses of high-dimensional multivariate data where the …
modeling tool for unsupervised analyses of high-dimensional multivariate data where the …
Contextual collaborative filtering via hierarchical matrix factorization
Matrix factorization (MF) has been demonstrated to be one of the most competitive
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …
techniques for collaborative filtering. However, state-of-the-art MFs do not consider …