Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Ju, Bin; Qian, Yuntao; Ye, Minchao; Ni, Rong; Zhu, Chenxi

doi:10.1371/journal.pone.0135090

Cited by 17 publications

(8 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decomposed user low-rank matrix can be seen as a representation of user preferences. The capability of capturing the user preferences have been confirmed by [42] and [43].…”

Section: B User Preference Modelsmentioning

confidence: 86%

Movie Recommendation via Markovian Factorization of Matrix Processes

Zhang

Mao

2019

IEEE Access

View full text Add to dashboard Cite

The success of the probabilistic matrix factorization (PMF) model has inspired the rapid development of collaborative filtering algorithms, among which timeSVD++ has demonstrated great performance advantage in solving the movie rating prediction problem. Allowing the model to evolve over time, timeSVD++ accounts for ''concept drift'' in collaborative filtering by heuristically modifying the quadratic optimization problem derived from the PMF model. As such, timeSVD++ no longer carries any probabilistic interpretation. This lack of frameworks makes the generalization of timeSVD++ to other collaborative filtering problems rather difficult. This paper presents a new model family termed Markovian factorization of matrix process (MFMP). On one hand, MFMP models, such as timeSVD++, are capable of capturing the temporal dynamics in the dataset, and on the other hand, they also have clean probabilistic formulations, allowing them to adapt to a wide spectrum of collaborative filtering problems. Two simple example models in this family are introduced for the prediction of movie ratings using time-stamped rating data. The experimental study using MovieLens dataset demonstrates that the two models, although simple and primitive, already have comparable or even better performance than timeSVD++ and a standard tensor factorization model. INDEX TERMS Recommender system, collaborative filtering, matrix factorization.

show abstract

“…The decomposed user low-rank matrix can be seen as a representation of user preferences. The capability of capturing the user preferences have been confirmed by [42] and [43].…”

Section: B User Preference Modelsmentioning

confidence: 86%

Movie Recommendation via Markovian Factorization of Matrix Processes

Zhang

Mao

2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…-Bayesian Temporal Matrix Factorization (BTMF) (Zhang et al 2014): This is a Bayesian temporal MF approach that captures the temporal dynamics of user preferences by learning a transition matrix for each user latent feature vectors between two successive time periods. -Dynamic Multi-Task Non-Negative Matrix Factorization (DMNMF) (Ju et al 2015):…”

Section: Comparison Methodsmentioning

confidence: 99%

“…The method learns a linear model to extract the transition pattern for each user's latent feature vector using Lasso regression. An approach based on multi-task non-negative MF was presented in Ju et al (2015) that uses a transition matrix to map between latent features of users in two successive time periods in order to track the temporal dynamics of user preferences. The transition matrix used in this method needs to be fixed, while in practice, this matrix is different for each user and each time period.…”

Section: Introductionmentioning

confidence: 99%

TSCMF: Temporal and social collective matrix factorization model for recommender systems

2020

View full text Add to dashboard Cite

In real-world recommender systems, user preferences are dynamic and typically change over time. Capturing the temporal dynamics of user preferences is essential to design an efficient personalized recommender system and has recently attracted significant attention. In this paper, we consider user preferences change individually over time. Moreover, based on the intuition that social influence can affect the users' preferences in a recommender system, we propose a Temporal and Social Collective Matrix Factorization model called TSCMF for recommendation. We jointly factorize the users' rating information and social trust information in a collective matrix factorization framework by introducing a joint objective function. We model user dynamics into this framework by learning a transition matrix of user preferences between two successive time periods for each individual user. We present an efficient optimization algorithm based on stochastic gradient descent for solving the objective function. The experiments on a real-world dataset illustrate that the proposed model outperforms the competitive methods. Moreover, the complexity analysis demonstrates that the proposed model can be scaled up to large datasets.

show abstract

“…We use GFA as a comparison both as is and with a simple correction for nonnegativity. One similar work [9] tries to model the temporal dynamics but only takes the dynamics of the left-hand side matrix into account, the loading matrix is considered static.…”

Section: Related Backgroundmentioning

confidence: 99%

Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining

Peltonen

Nummenmaa

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

Nonnegative matrix factorization (NMF) is a popular approach to model data, however, most models are unable to flexibly take into account multiple matrices across sources and time or apply only to integer-valued data. We introduce a probabilistic, Gaussian Process based, more inclusive NMF-based model which jointly analyzes nonnegative data such as text data word content from multiple sources in a temporal dynamic manner. The model collectively models observed matrix data, source-wise latent variables and their dependencies and temporal evolution with a full-fledged hierarchical approach including flexible nonparametric temporal dynamics. Experiments on simulated data and real data show the model outperforms comparable models. A case study on social media and news demonstrates the model discovers semantically meaningful topical factors and their evolution. CCS CONCEPTS • Computing methodologies → Non-negative matrix factorization; Natural language processing.

show abstract

Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Cited by 17 publications

References 29 publications

Movie Recommendation via Markovian Factorization of Matrix Processes

Movie Recommendation via Markovian Factorization of Matrix Processes

TSCMF: Temporal and social collective matrix factorization model for recommender systems

Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining

Contact Info

Product

Resources

About