Modeling Time-Series and Spatial Data for Recommendations and Other Applications

• URL: http://arxiv.org/abs/2212.13259v1
• Date: Sun, 25 Dec 2022 09:34:15 GMT
• Title: Modeling Time-Series and Spatial Data for Recommendations and Other Applications
• Authors: Vinayak Gupta
• Abstract summary: We address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. We extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction.
• Score: 1.713291434132985
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the research directions described in this thesis, we seek to address the critical challenges in designing recommender systems that can understand the dynamics of continuous-time event sequences. We follow a ground-up approach, i.e., first, we address the problems that may arise due to the poor quality of CTES data being fed into a recommender system. Later, we handle the task of designing accurate recommender systems. To improve the quality of the CTES data, we address a fundamental problem of overcoming missing events in temporal sequences. Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i.e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in. Lastly, we highlight that the capabilities of the proposed models can have applications beyond recommender systems, and we extend their abilities to design solutions for large-scale CTES retrieval and human activity prediction. A significant part of this thesis uses the idea of modeling the underlying distribution of CTES via neural marked temporal point processes (MTPP). Traditional MTPP models are stochastic processes that utilize a fixed formulation to capture the generative mechanism of a sequence of discrete events localized in continuous time. In contrast, neural MTPP combine the underlying ideas from the point process literature with modern deep learning architectures. The ability of deep-learning models as accurate function approximators has led to a significant gain in the predictive prowess of neural MTPP models. In this thesis, we utilize and present several neural network-based enhancements for the current MTPP frameworks for the aforementioned real-world applications.

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