Search:
Computing and Library Services - delivering an inspiring information environment

Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information

Yao, Lina, Sheng, Quan Z., Wang, Xianzhi, Zhang, Emma Wei and Qin, Yongrui (2017) Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information. ACM Transactions on Internet Technology (TOIT). ISSN 1533-5399 (In Press)

[img]
Preview
PDF - Accepted Version
Download (4MB) | Preview

Abstract

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this paper, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users’ check-ins instead of the traditional two dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data which is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.

Item Type: Article
Additional Information: © ACM, YYYY. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution
Subjects: Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
Related URLs:
Depositing User: Yongrui Qin
Date Deposited: 13 Oct 2017 10:57
Last Modified: 13 Oct 2017 11:02
URI: http://eprints.hud.ac.uk/id/eprint/33686

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©