Oct 4, 2017 · In this paper, we proposed a Social Personalized Ranking Embedding (SPRE) model, which integrates user personalization and social relations into consideration.
[PDF] Personalized Ranking Metric Embedding for Next New POI ...
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In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current lo- cation are to be recommended. The challenge ...
We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences.
Our experiments on a real-world large-scale dataset (Foursquare) results show that our model outperforms the state-of-the-art next POI recommendation methods.
Aug 1, 2024 · PRME [12]: Utilizing a pairwise ranking metric embedding, this personalized ranking model effectively learns sequential transi- tions of POIs ...
This paper proposes a personalized ranking metric embedding method (PRME) to model personalized check-in sequences and develops a PRME-G model, ...
Yan Long, Pengpeng Zhao, Victor S. Sheng, Guanfeng Liu , Jiajie Xu, Jian Wu, Zhiming Cui: Social Personalized Ranking Embedding for Next POI Recommendation.
Our experiments on a real-world large-scale dataset (Foursquare) results show that our model outperforms the state-of-the-art next POI recommendation methods.
We address the problem of next POI recommendation through a two-fold approach. •. A Listwise Bayesian Personalized Ranking (LBPR) approach is proposed.
Dec 17, 2020 · Next, the Bayesian personalized ranking matrix factorization model is proposed to select POI that is close to the user's preference from each ...