Construction and Adaptability Analysis of User's Preference Models Based on Check-in Data in LBSN

TL;DR

This paper develops a multi-view preference modeling approach using check-in data in LBSN, introduces a validation algorithm for model applicability, and employs a CNN-based unified model, validated on multiple datasets.

Contribution

It proposes a novel multi-view preference model, a validation algorithm based on stability, and a CNN-based unified model for user preference analysis.

Findings

The method effectively validates preference models.

The CNN-based unified model accurately characterizes model applicability.

Validation results demonstrate the approach's effectiveness.

Abstract

With the widespread use of mobile phones, users can share their location anytime, anywhere, as a form of check-in data. These data reflect user preferences. Furthermore, the preference rules for different users vary. How to discover a user's preference from their related information and how to validate whether a preference model is suited to a user is important for providing a suitable service to the user. This study provides four main contributions. First, multiple preference models from different views for each user are constructed. Second, an algorithm is proposed to validate whether a preference model is applicable to the user by calculating the stability value of the user's long-term check-in data for each model. Third, a unified model, i.e., a multi-channel convolutional neural network is used to characterize this applicability. Finally, three datasets from multiple sources are used to verify the validity of the method, the results of which show the effectiveness of the method.