Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation

Jing He; Xin Li; Lejian Liao; Mingzhong Wang

doi:10.1007/978-3-030-10928-8_44

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Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation

Conference paper

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Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation

Jing He, Xin Li, Lejian Liao and Mingzhong Wang

Joint European Conference on Machine Learning and Knowledge Discovery in Databases ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases, Vol.Part II, pp.741-756

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2018 (Dublin, Ireland, 10-Sep-2018–14-Sep-2018)

Lecture Notes in Computer Science (LNCS), 11052, Springer Nature Switzerland AG

2018

DOI: https://doi.org/10.1007/978-3-030-10928-8_44

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Abstract

point-of-interest

recommendation

probabilistic factor analysis model

Temporal information plays an important role in Point-ofInterest (POI) recommendations. Most existing studies model the temporal influence by utilizing the observed check-in time stamps explicitly. With the conjecture that transition intervals between successive checkins may carry more information for diversified behavior patterns, we propose a probabilistic factor analysis model to incorporate three components, namely, personal preference, distance preference, and transition interval preference. They are modeled by an observed third-rank transition tensor, a distance constraint, and a continuous latent variable, respectively. In our framework, the POI recommendation and the transition interval for user's very next move can be inferred simultaneously by maximizing the posterior probability of the overall transitions. Expectation Maximization (EM) algorithm is used to tune the model parameters. We demonstrate that the proposed methodology achieves substantial gains in terms of prediction on next move and its expected time over state-of-the-art baselines.

Details

Title: Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation
Authors: Jing He (Author) - Beijing Institute of Technology, China
Xin Li (Author) - Beijing Institute of Technology, China
Lejian Liao (Author) - Beijing Institute of Technology, China
Mingzhong Wang (Author) - University of the Sunshine Coast - Faculty of Arts, Business and Law
Contributors: Michele Berlingerio (Editor)
Francesco Bonchi (Editor)
Thomas Gärtner (Editor)
Neil Hurley (Editor)
Georgiana Ifrim (Editor)
Publication details: Joint European Conference on Machine Learning and Knowledge Discovery in Databases ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases, Vol.Part II, pp.741-756
Conference details: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2018 (Dublin, Ireland, 10-Sep-2018–14-Sep-2018)
Series: Lecture Notes in Computer Science (LNCS); 11052
Publisher: Springer Nature Switzerland AG
Date published: 2018
DOI: 10.1007/978-3-030-10928-8_44
Organisation Unit: University of the Sunshine Coast, Queensland; USC Business School - Legacy; School of Science, Technology and Engineering
Language: English
Record Identifier: 99451005902621
Output Type: Conference paper

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