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استفاده از مدل های مولد ژرف برای تولید دنباله رخدادها در سامانه های توصیه گر
حقی پور، امیر شایان Haghipour, Amir Shayan
Using Deep generative Models for Event Sequence Generation in Recommender Systems
Haghipour, Amir Shayan | 2019
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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 52549 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Soleimani, Mahdieh; Rabeei, Hamid Reza
- Abstract:
- In a variety of applications, we deal with event sequences which we require to model the time of those. Event sequences modelling is vital in a variety of applications such as electronic commerce, social networks, health information; For instance, in the context of social networks entrance time, the act of like or comment can be regarded as an event sequence. Point processes are the framework for modelling event sequences, in which designer use prior knowledge and different assumptions (which is not necessarily true) to set the functional form of intensity function. That functional form may not be sufficient enough to model event sequences. In this project, we have used a deep nonlinear model instead of a specific parametric form to find a better representation in shaping and estimating of events. Based on deep analysis of previous work, we have proposed novel loss function to model temporal user behaviour more accurately and intuitively. For performance evaluation of the model, implementation on the Last.fm dataset has been done and comparison with previous baselines based on the measures such as root mean squared error and mean absolute error in return time prediction has been performed
- Keywords:
- Neural Networks ; Recommender System ; Generative Models ; Event Sequence Generation
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