Context-aware location recommendation by using a random walk-based approach
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 …
and share it with other users. The accumulated check-in data can be employed for the …
Recommending travel packages based on mobile crowdsourced data
Mobile crowdsoured data from location based social network services (LBSNs) provide
information on individual's preferences for locations. In this article, we propose a travel …
information on individual's preferences for locations. In this article, we propose a travel …
Social bridges in urban purchase behavior
The understanding and modeling of human purchase behavior in city environment can have
important implications in the study of urban economy and in the design and organization of …
important implications in the study of urban economy and in the design and organization of …
Location recommendation by combining geographical, categorical, and social preferences with location popularity
The primary aim of location recommendation is to predict users' future movement by
modeling user preference. Multiple types of information have been adopted in profiling …
modeling user preference. Multiple types of information have been adopted in profiling …
Geo-social recommendations based on incremental tensor reduction and local path traversal
P Symeonidis, A Papadimitriou… - Proceedings of the 3rd …, 2011 - dl.acm.org
Social networks have evolved with the combination of geographical data, into Geo-social
networks (GSNs). GSNs give users the opportunity, not only to communicate with each other …
networks (GSNs). GSNs give users the opportunity, not only to communicate with each other …
SocialMix: A familiarity-based and preference-aware location suggestion approach
Traditionally, location suggestion systems have employed collaborative filtering model to
make recommendations for users based on data gathered from users with similar interests …
make recommendations for users based on data gathered from users with similar interests …
A hybrid multigroup coclustering recommendation framework based on information fusion
S Huang, J Ma, P Cheng, S Wang - ACM Transactions on Intelligent …, 2015 - dl.acm.org
Collaborative Filtering (CF) is one of the most successful algorithms in recommender
systems. However, it suffers from data sparsity and scalability problems. Although many …
systems. However, it suffers from data sparsity and scalability problems. Although many …
Joint modeling of participant influence and latent topics for recommendation in event-based social networks
Event-based social networks (EBSNs) are becoming popular in recent years. Users can
publish a planned event on an EBSN website, calling for other users to participate in the …
publish a planned event on an EBSN website, calling for other users to participate in the …
Anomaly scoring using collaborative filtering
I Argoeti, R Levin, JM Monsonego - US Patent 11,310,257, 2022 - Google Patents
(57) ABSTRACT A machine learning model is trained using tuples that identify an actor, a
resource, and a rating based on a normalized count of the actor's attempts to access the …
resource, and a rating based on a normalized count of the actor's attempts to access the …
Unifying virtual and physical worlds: Learning toward local and global consistency
Event-based social networking services, such as Meetup, are capable of linking online
virtual interactions to offline physical activities. Compared to mono online social networking …
virtual interactions to offline physical activities. Compared to mono online social networking …