Modeling Temporal Adoptions Using Dynamic Matrix Factorization

Chua, Freddy Chong Tat; Oentaryo, Richard J.; Lim, Ee-Peng

doi:10.1109/icdm.2013.25

Cited by 41 publications

(45 citation statements)

References 14 publications

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“…Improvements of RMSE when compared to time SVD [156] are reported. Consistent results are reported in [52], after offline evaluation using real data.…”

Section: Cold Start Problemsupporting

confidence: 75%

Using Contextual Information to Understand Searching and Browsing Behavior

Kiseleva

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Improvements of RMSE when compared to time SVD [156] are reported. Consistent results are reported in [52], after offline evaluation using real data.…”

Section: Cold Start Problemsupporting

confidence: 75%

Using Contextual Information to Understand Searching and Browsing Behavior

Kiseleva

2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…[13,20] integrated the user social network and/or the item-item similarity with the user-item matrix in the probabilistic factor analysis. Recently, dynamic matrix factorization methods have been proposed to handle the user preferences changing over time [5,19]. [19] built a dynamic state space model upon probabilistic matrix factorization to track the temporal dynamics of the user latent factor.…”

Section: Related Workmentioning

confidence: 99%

“…[19] built a dynamic state space model upon probabilistic matrix factorization to track the temporal dynamics of the user latent factor. [5] applied non-negative matrix factorization on linear dynamic systems, to model evolving user preferences on the user-item adoption problem.…”

Section: Related Workmentioning

confidence: 99%

“…Given a network of time series R, the dimension of latent factors l, and the weight of contextual information λ, the algorithm aims to tune model parameters and approximate the values of the latent factors. It first randomly initializes the model parameters θ (including U) (step 1); and then iteratively infers the expectations of Z, V (step [3][4][5][6][7][8][9][10][11], and updates the model parameters θ (step 13) until convergence. The missing values can be inferred from the reconstructed time seriesX (step 16).…”

Section: The Overall Algorithmmentioning

confidence: 99%

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Fast Mining of a Network of Coevolving Time Series

Cai¹,

Tong²,

Fan³

et al. 2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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Coevolving multiple time series are ubiquitous and naturally appear in a variety of high-impact applications, ranging from environmental monitoring, computer network traffic monitoring, motion capture, to physiological signal in health care and many more. In many scenarios, the multiple time series data is often accompanied by some contextual information in the form of networks. In this paper, we refer to such multiple time series, together with its embedded network as a network of coevolving time series. In order to unveil the underlying patterns of a network of coevolving time series, we propose DCMF, a dynamic contextual matrix factorization algorithm. The key idea is to find the latent factor representation of the input time series and that of its embedded network simultaneously. Our experimental results on several real datasets demonstrate that our method (1) outperforms its competitors, especially when there are lots of missing values; and (2) enjoys a linear scalability w.r.t. the length of time series.

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“…In contrast, our transition matrices are time-invariant and are learnt automatically by the model from observed data. [1] further extended [15] in the scenario of temporal adoption.…”

Section: Related Workmentioning

confidence: 99%

Latent Factor Transition for Dynamic Collaborative Filtering

Zhang¹,

Wang²,

Yu³

et al. 2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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User preferences change over time and capturing such changes is essential for developing accurate recommender systems. Despite its importance, only a few works in collaborative filtering have addressed this issue. In this paper, we consider evolving preferences and we model user dynamics by introducing and learning a transition matrix for each user's latent vectors between consecutive time windows. Intuitively, the transition matrix for a user summarizes the time-invariant pattern of the evolution for the user. We first extend the conventional probabilistic matrix factorization and then improve upon this solution through its fully Bayesian model. These solutions take advantage of the model complexity and scalability of conventional Bayesian matrix factorization, yet adapt dynamically to user's evolving preferences. We evaluate the effectiveness of these solutions through empirical studies on six large-scale real life data sets.

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Modeling Temporal Adoptions Using Dynamic Matrix Factorization

Cited by 41 publications

References 14 publications

Using Contextual Information to Understand Searching and Browsing Behavior

Using Contextual Information to Understand Searching and Browsing Behavior

Fast Mining of a Network of Coevolving Time Series

Latent Factor Transition for Dynamic Collaborative Filtering

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