JOURNAL ARTICLE

A Multi-objective Framework for Location Recommendation Based on User Preference

Abstract

Location recommendation has attracted increasing attention in recent years. This paper proposes a novel multi-objective framework for location recommendation based on user preference. Under this framework, user preference can be separated into common preference and individual preference. Then two contradictory objective functions are designed to describe these two kinds of preferences. It is difficult to optimize these two objective functions simultaneously. In this paper, a novel multi-objective evolutionary algorithm is proposed to optimize these two objective functions. The proposed algorithm can make a good balance between these two objective functions. Experiments on two real application recommendation scenarios: Foursquare dataset and Gowalla dataset show that the proposed algorithm is effective to recommend locations.

Keywords:
Preference Computer science Recommender system Data mining Machine learning Artificial intelligence Information retrieval Mathematics Statistics

Metrics

7
Cited By
2.80
FWCI (Field Weighted Citation Impact)
14
Refs
0.92
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Recommender Systems and Techniques
Physical Sciences →  Computer Science →  Information Systems
Data Management and Algorithms
Physical Sciences →  Computer Science →  Signal Processing
Multi-Criteria Decision Making
Social Sciences →  Decision Sciences →  Management Science and Operations Research

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