Affiliation:
1. School of Electronics and Information Technology, Sun Yat-Sen University, China
2. Tencent Inc. China
3. Department of Electrical Engineering, Tsinghua University, China
Abstract
With the rapid development of Internet services and mobile devices, nowadays, users can connect to online services anytime and anywhere. Naturally, user's online activity behavior is coupled with time and location contexts and highly influenced by them. Therefore, personalized context-aware online activity modelling and prediction is very meaningful and necessary but also very challenging, due to the complicated relationship between users, activities, spatial and temporal contexts and data sparsity issues. To tackle the challenges, we introduce offline check-in data as auxiliary data and build a user-location-time-activity 4D-tensor and a location-time-POI 3D-tensor, aiming to model the relationship between different entities and transfer semantic features of time and location contexts among them. Accordingly, in this paper we propose a transfer learning based collaborative tensor factorization method to achieve personalized context-aware online activity prediction. Based on real-world datasets, we compare the performance of our method with several state-of-the-arts and demonstrate that our method can provide more effective prediction results in the high sparsity scenario. With only 30% of observed time and location contexts, our solution can achieve 40% improvement in predicting user's Top5 activity behavior in new time and location scenarios. Our study is the first step forward for transferring knowledge learned from offline check-in behavior to online activity prediction to provide better personalized context-aware recommendation services for mobile users.
Funder
the National Nature Science Foundation of China
the research fund of Tsinghua University-Tencent Joint Laboratory for Internet Innovation Technology
Beijing National Research Center for Information Science and Technology
Beijing Natural Science Foundation
the MOE-CMCC Joint Research Fund of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
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