Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2892289 | IEEE ACCESS |
Keywords | Field | DocType |
Recommender system,collaborative filtering,matrix factorization | Heuristic,Collaborative filtering,Computer science,MovieLens,Algorithm,Concept drift,Factorization,Probabilistic logic,Quadratic programming,Hidden Markov model,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richong Zhang | 1 | 232 | 39.67 |
| Yongyi Mao | 2 | 524 | 61.02 |