Developing recommendation techniques for location based social networks using random walk

Download
index.pdf
2015
Bağcı, Hakan
Show full item record
243
views
107
downloads
The location-based social networks (LBSN) enable users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this thesis, we propose three recommendation algorithms for location-based social networks. These are random walk based context-aware location (CLoRW), activity (RWCAR) and friend (RWCFR) recommendation algorithms. All the algorithms consider the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. We propose an undirected unweighted graph model for representing LBSN data that contains users, locations and activities. We build a graph according to the current context of the user for each algorithm depending on this LBSN model. A random walk with restart approach is employed on this graph to predict the recommendation scores. Lists of users, locations and activities are recommended to users after ordering the nodes according to estimated scores. We compare CLoRW with popularity-based, friend-based and expert-based baselines, collaborative filtering approach and a similar work in the literature. According to results, our location recommendation algorithm outperforms these approaches in all of the test cases. Moreover, we also compare RWCAR and RWCFR algorithms with respective popularity-based, friend-based and expert-based baselines. In all of the experiments, RWCAR and RWCFR perform better than the baselines. The results clearly indicate that random walk based context-aware recommendation approach is a good candidate for recommending locations, activities and friends for LBSNs.
Mobile computing., Context-aware computing., Location-based services., Online social networks., Random walks (Mathematics)., Information filtering systems., Recommender systems (Information filtering).
http://etd.lib.metu.edu.tr/upload/12619643/index.pdf
https://hdl.handle.net/11511/25364
Graduate School of Natural and Applied Sciences, Thesis

Suggestions

Context-aware location recommendation by using a random walk-based approach
Bagci, Hakan; Karagöz, Pınar (2016-05-01)
The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized ...
Context aware friend recommendation for location based social networks using random walk
Bağcı, Hakan; Karagöz, Pınar (null; 2016-04-10)
The location-based social networks (LBSN) facilitate users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a random walk based context-aware friend recommendation algorithm (RWCFR). RWCFR considers the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. Our LBSN...
TRUST-AWARE LOCATION RECOMMENDATION IN LOCATION-BASED SOCIAL NETWORKS
Cantürk, Deniz; Karagöz, Pınar; Department of Computer Engineering (2021-8-9)
Users can share their location with other social network users through location-embedded information in LBSNs (Location-Based Social Network). LBSNs contain useful resources, such as user check-in activities, for building a personalized recommender system. Trust in social networks is another important concept that has been integrated into a recommendation system in various settings. In this thesis, we propose two novel techniques for location recommendation, TLoRW and SgWalk, to improve recommendation perfo...
A User modeling and recommendation system by means of social networks
Karakaya, Ali; Çiçekli, Fehime Nihan; Department of Computer Engineering (2014)
In this thesis, it is aimed to design a system which builds user profiles to model users’ preferences by tracking the activities of the users on social networks. Specifically, Facebook and Twitter are considered as the social networks. The extracted user profiles are used in a recommendation system application. The user data collected from the social networks is enriched with the concepts in Freebase which is an online and public library, and then the enriched data is used to create vector-based and graph-b...
Analyzing and Predicting Privacy Settings in the Social Web
Naini, Kaweh Djafari; Altıngövde, İsmail Sengör; Kawase, Ricardo; Herder, Eelco; Niederee, Claudia (2015-07-03)
Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Alt...
Citation Formats
H. Bağcı, “Developing recommendation techniques for location based social networks using random walk,” Ph.D. - Doctoral Program, Middle East Technical University, 2015.
METU IIS - Integrated Information Service open@metu.edu.tr