Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach
| Autor: | Wancheng Ni, Haidong Zhang, Yiping Yang, Xin Li |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Markov chain
Computer science business.industry Context (language use) 02 engineering and technology Recommender system Machine learning computer.software_genre Computer Science Applications Data modeling Human-Computer Interaction Control and Systems Engineering 020204 information systems Expectation–maximization algorithm 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Data mining Hidden semi-Markov model Artificial intelligence Electrical and Electronic Engineering Hidden Markov model business computer Software |
| Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 48:177-194 |
| ISSN: | 2168-2232 2168-2216 |
| Popis: | Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users’ preferences often change over time, leading to studies on time-dependent recommender systems. However, most existing approaches that deal with time information remain primitive. In this paper, we extend existing methods and propose a hidden semi-Markov model to track the change of users’ interests. Particularly, this model allows for capturing the different durations of user stays in a (latent) interest state, which can better model the heterogeneity of user interests and focuses. We derive an expectation maximization algorithm to estimate the parameters of the framework and predict users’ actions. Experiments on three real-world datasets show that our model significantly outperforms the state-of-the-art time-dependent and static benchmark methods. Further analyses of the experiment results indicate that the performance improvement is related to the heterogeneity of state durations and the drift of user interests in the dataset. |
| Databáze: | OpenAIRE |
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