Spatiotemporal activity modeling under data scarcity: A graph-regularized cross-modal embedding approach

Chao Zhang, Mengxiong Liu, Zhengchao Liu, Carl Yang, Luming Zhang, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Spatiotemporal activity modeling, which aims at modeling users' activities at different locations and time from user behavioral data, is an important task for applications like urban planning and mobile advertising. State-of-the-art methods for this task use cross-modal embedding to map the units from different modalities (location, time, text) into the same latent space. However, the success of such methods relies on data sufficiency, and may not learn quality embeddings when user behavioral data is scarce. To address this problem, we propose BRANCHNET, a spatiotemporal activity model that transfers knowledge from external sources for alleviating data scarcity. BRANCHNET adopts a graph-regularized cross-modal embedding framework. At the core of it is a main embedding space, which is shared by the main task of reconstructing user behaviors and the auxiliary graph embedding tasks for external sources, thus allowing external knowledge to guide the cross-modal embedding process. In addition to the main embedding space, the auxiliary tasks also have branched task-specific embedding spaces. The branched embeddings capture the discrepancies between the main task and the auxiliary ones, and free the main embeddings from encoding information for all the tasks. We have empirically evaluated the performance of BRANCHNET, and found that it is capable of effectively transferring knowledge from external sources to learn better spatiotemporal activity models and outperforming strong baseline methods.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI Press
Pages531-538
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Zhang, C., Liu, M., Liu, Z., Yang, C., Zhang, L., & Han, J. (2018). Spatiotemporal activity modeling under data scarcity: A graph-regularized cross-modal embedding approach. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 531-538). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI Press.