PCRM: Increasing POI Recommendation Accuracy in Location-Based Social Networks

• Lianggui Liu,
• Wei Li,
• Lingmin Wang,
• Huiling Jia,

Vol. 12, No. 11, November 29, 2018
10.3837/tiis.2018.11.010, Download Paper (Free):

• location-based social networks
• Point-of-Interest recommendation
• geographical area
• user's preference

Abstract

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users’ activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user’s activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

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