Journal ArticleDOI
Incorporating Spatial, Temporal, and Social Context in Recommendations for Location-Based Social Networks
TLDR
A novel approach to incorporate spatial, temporal, and social context into a traditional collaborative filtering algorithm is introduced, and it is demonstrated that this approach is at the least competitive with existing state-of-the-art location recommenders.Abstract:
Location-based social networks (LBSNs) such as Foursquare, Brightkite, and Gowalla are a growing area where recommendation algorithms find a practical application. With an ever-increasing variety of venues to choose from deciding on a destination can be overwhelming. Recommenders aid their users in the decision-making process by providing a list of locations likely to be relevant to the user’s needs and interests. Traditional collaborative filtering algorithms consider relationships between users and locations, finding users to be similar only if their location histories overlap. However, the availability of spatial, temporal, and social information in an LBSN offers an opportunity to improve the quality of a recommendation engine. Social network data allows us to connect users who can directly influence each other’s decisions. Temporal data allows us to account for the drifting preferences of users, giving more weight to recent location visits over historical selections, and taking advantages of repetitive behaviors. Spatial information allows us to focus recommendations on locations close to the user, keeping our recommendations relevant as a user travels. We introduce a novel approach to incorporate spatial, temporal, and social context into a traditional collaborative filtering algorithm. We evaluate our method on data sets collected from three LBSNs, and demonstrate that our approach is at the least competitive with existing state-of-the-art location recommenders.read more
Citations
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Patent
Inferring user preferences from an internet based social interactive construct
TL;DR: In this paper, the authors described improved capabilities for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of ascertaining preferences of a plurality of users who are part of an internet-based social interactive construct.
Journal ArticleDOI
Decentralized Autonomous Organizations: Concept, Model, and Applications
TL;DR: This article strives to present a systematic introduction of DAO, including its concept and characteristics, research framework, typical implementations, challenges, and future trends, including a novel reference model for DAO which employs a five-layer architecture.
Book ChapterDOI
Social Aware Cognitive Radio Networks: Effectiveness of Social Networks as a Strategic Tool for Organizational Business Management
TL;DR: This chapter delves into the cognitive radio (CR) and its social relations and makes sufficient exploits in establishing a scheme that will be based on social-based cooperative sensing scheme (SBC).
Book ChapterDOI
LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation
Hossein A. Rahmani,Mohammad Aliannejadi,Sajad Ahmadian,Mitra Baratchi,Mohsen Afsharchi,Fabio Crestani +5 more
TL;DR: An effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region and is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation.
Journal ArticleDOI
Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation
TL;DR: A new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs) is presented and the obtained results are found to be proficient by means of improved diversity and accuracy of generated recommendations.
References
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Book
Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
TL;DR: This book describes how music recommenders work, explores some of the limitations seen in current recommenders, offers techniques for evaluating the effectiveness of music recommendations and demonstrates how to build effective recommenders by offering two real-world recommender examples.
Proceedings ArticleDOI
Evaluating location predictors with extensive Wi-Fi mobility data
TL;DR: The results of the first extensive empirical evaluation of location predictors, using a two-year trace of the mobility patterns of over 6,000 users on Dartmouth's campus-wide Wi-Fi wireless network, are reported on.
Proceedings ArticleDOI
Location recommendation in location-based social networks using user check-in data
TL;DR: This paper proposes algorithms that create recommendations based on four factors: a) past user behavior (visited places), b) the location of each venue, c) the social relationships among the users, and d) the similarity between users.