Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

Jun Zeng (Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, China), Feng Li (Graduate School of Software Engineering, Chongqing University, Chongqing, China), Xin He (Chongqing University, Chongqing, China) and Junhao Wen (Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China & School of Software Engineering, Chongqing University, Chongqing, China)
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJWSR.2019100103

Abstract

Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs), e.g., Foursquare, Brightkite. It helps users explore the surroundings and help POI owners increase income. While several researches have been proposed for the recommendation services, it lacks integrated analysis on POI recommendation. In this article, the authors propose a unified recommendation framework, which fuses personalized user preference, geographical influence, and social reputation. The TF-IDF method is adopted to measure the interest level and contribution of locations when calculating the similarity between users. Geographical influence includes geographical distance and location popularity. The authors find friends in Brightkite share low common visited POIs. It means friends' interests may vary greatly. Instead of directly getting recommendations from so-called friends in LBSN, the users attain recommendation from others according to their reputation. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.
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Introduction

In location-based social networks (LBSNs), people can share their position and activities to other users through check-ins. Generally, a check-in includes user information, time, location and some reviews. Different from traditional ratings for items, user check-ins implicitly demonstrate user preference. Mass check-in data provides an opportunity to mining user preference and enables lots of location-aware services as shown in Figure 1. Point of interest (POI) recommendation is one of such services, which is aiming to recommend new places which users have not visited before when they explore their surroundings. POI recommendation not only helps users identify favorite locations, but also help POI owner acquire more target customers (Zhang & Chow, 2016; Liu, Tuan-Anh, Gao, & Yuan, 2017).

Figure 1.

The location based social network

IJWSR.2019100103.f01

POI recommendation has attracted more and more researchers’ attention (Zeng, Li, Wen, & Zhou, 2017). Collaborative filtering (CF) is widely adopted to POI recommendation. Ye, Yin, Lee, and Lee (2011) compared user-based and item-based method to make POI recommendation. Their experimental results show user-based approach performs better than item-based approach. In this case, there are much work about user-based CF (Liu, Ma, Chen, & Xiong, 2013; Ye, Yin, & Lee, 2010; Bao, Zheng, & Mokbel, 2012; Yuan, Cong, Ma, Sun, & Thalmann, 2013; Ye et al., 2011) and model-based CF (Gao, Tang, Hu, & Liu, 2015; Lian et al., 2014; Liu, Fu, Yao, & Xiong, 2013; Gao, Tang, Hu, & Liu, 2013; Hu, Sun, & Liu, 2014; Yuan, Cong, & Sun, 2014; Tang, Hu, Gao, & Liu, 2013; Cho, Myers, & Leskovec, 2011). For example, Ye et al. (2011) thought social relationship are incorporated into recommendation. Ye et al. (2010) proposed Friend-based CF (FCF), which directly recommends POIs for users from those locations their friends visited before. Compared with traditional user-based CF (user-CF), this method has lower computational overhead. However, the method is limited because the tastes of a user’s friends may vary greatly. Cheng, Yang, King, and Lyu (2012) found the average check-ins overlap between friends is about 9.6%. This indicates fiends may has different interest. Some matrix factorization (MF) based methods can easily fuse factors, such as geographical distance and social relation, but the optimal result is greatly affected by all kinds of parameters.

In this paper, we propose a novel method which fused geographical influence and social reputation to improve POI recommendation. The main contributions are as follows:

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