Proceedings ArticleDOI
Time-aware point-of-interest recommendation
Quan Yuan,Gao Cong,Zongyang Ma,Aixin Sun,Nadia Magnenat Thalmann +4 more
- pp 363-372
Reads0
Chats0
TLDR
This paper defines a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day, and develops a collaborative recommendation model that is able to incorporate temporal information.Abstract:
The availability of user check-in data in large volume from the rapid growing location based social networks (LBSNs) enables many important location-aware services to users. Point-of-interest (POI) recommendation is one of such services, which is to recommend places where users have not visited before. Several techniques have been recently proposed for the recommendation service. However, no existing work has considered the temporal information for POI recommendations in LBSNs. We believe that time plays an important role in POI recommendations because most users tend to visit different places at different time in a day, \eg visiting a restaurant at noon and visiting a bar at night. In this paper, we define a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day. To solve the problem, we develop a collaborative recommendation model that is able to incorporate temporal information. Moreover, based on the observation that users tend to visit nearby POIs, we further enhance the recommendation model by considering geographical information. Our experimental results on two real-world datasets show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.read more
Citations
More filters
Proceedings ArticleDOI
Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks
TL;DR: This paper proposes a novel knowledge enhanced sequential recommender that integrates the RNN-based networks with Key-Value Memory Network (KV-MN) and incorporates knowledge base information to enhance the semantic representation of KV- MN.
Proceedings Article
Personalized ranking metric embedding for next new POI recommendation
TL;DR: This paper proposes a personalized ranking metric embedding method (PRME) to model personalized check-in sequences and develops a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance.
Proceedings ArticleDOI
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
TL;DR: A ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges of scarcity of check-in data and context information, and outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
Proceedings ArticleDOI
Learning Graph-based POI Embedding for Location-based Recommendation
TL;DR: A generic graph-based embedding model is proposed that jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs into a shared low dimensional space and develops a novel time-decay method to dynamically compute the user's latest preferences.
Proceedings ArticleDOI
Exploiting Geographical Neighborhood Characteristics for Location Recommendation
TL;DR: This paper proposes a novel recommendation approach, namely Instance-Region Neighborhood Matrix Factorization (IRenMF), which exploits two levels of geographical neighborhood characteristics: a) instance-level characteristics, i.e., nearest neighboring locations tend to share more similar user preferences; and b) region- level characteristics,i.e, locations in the same geographical region may share similaruser preferences.
References
More filters
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
A survey of collaborative filtering techniques
TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Proceedings ArticleDOI
Friendship and mobility: user movement in location-based social networks
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.