Time-aware point-of-interest recommendation

By Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, Nadia Magnenat-Thalmann. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013

807 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/6cd49d50eca5aabec277c0075f8394bdfbe64730

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Citations in the corpus

Recommender Systems (2016, neutral): “Time-sensitive methods for location-based recommendation are discussed in [655].”
Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “Yuan et al. (Yuan et al. 2013) incorporated both temporal cyclic information and geographical information for time-aware POI recommendation.”
Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks (2018, background): “As several representative kinds of context, temporal information [38], social media information [40, 41] and knowledge information [37] have been utilized in recommendation.”
Recommendation (2020, positive): “Recent research has focused on recommending venues using collaborative-ﬁltering technique [8,15], where the system recommends venues based on users whose preferences are similar to those of the target user (i.e., the user who receives the recommendations).”
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, methodology): “Yuan et al. propose a method called UTE+SE, which extends the user-based CF to incorporate both the temporal and geographical eﬀects with a linear combination framework [24].”
Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, methodology): “Similar experimental settings have been used to validate the performances of location recommendation methods in previous work [3, 26, 31].”
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019, methodology): “Yuan et al.(2013) utilized the temporal preference to enhance the efﬁciency and effectiveness of the solution.”
SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews (2015, background): “Several proposals recommend POIs based on collaborative filtering (CF) [22], [9], [26].”
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (2014, background): “Geographical influence and temporal influence are major considerations in POI recommendation [25, 26].”
Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis (2015, methodology): “[37] Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann.”
Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation (2017, background): “While traditional recommender systems mostly work with ratings and reviews [20], POI recommendations naturally come with various context, such as geographical information [24, 25, 28, 47, 49], categorical information [53, 55], social information [22, 38, 50, 52], temporal information [48, 56], etc.”
Learning Graph-based POI Embedding for Location-based Recommendation (2016, background): “Speciﬁcally, to overcome the data sparsity issue, most prior work of location-based recommendation focused on exploiting the geographical inﬂuence [10, 3, 20] and temporal cyclic eﬀect [7, 28] to provide spatial or/and temporal context-aware recommendation.”
GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “…employed the prior preference of users to unvisited POIs as the weight of the edges between the users and POIs in the complete bipartite graph, while other studies utilize the prior preference to adjust the posterior preference that are derived from the geographical information [8, 13, 14, 28].”