Results 131 to 140 of about 1,026 (182)

Granulosa cell death is a significant contributor to DNA-damaging chemotherapy-induced ovarian insufficiency†. [PDF]

Biol Reprod
Kawano M, McKey J, Batchvarov IS, Capel B.   +3 more
europepmc   +1 more source

A user-embedded temporal attention neural network for IoT trajectories prediction. [PDF]

PeerJ Comput Sci
Feng D, Li S, Xiang Y, Zheng J.
europepmc   +1 more source

MTSA-SC: A multi-task learning approach for individual trip destination prediction with multi-trajectory subsequence alignment and space-aware loss functions. [PDF]

PLoS One
Luo D, Zhao F, Zhou H, Wang C, Xiong H.
europepmc   +1 more source

Women's healthcare providers' knowledge and practices surrounding fragile-X associated primary ovarian insufficiency (FXPOI). [PDF]

J Assist Reprod Genet
Singleton AL, Hipp HS, Ali N, Poteet B, Allen EG.   +4 more
europepmc   +1 more source

A method for planning cycling networks in traditional village contiguous areas using Wi-Fi probe-based attractiveness evaluation. [PDF]

Sci Rep
Liu S, Wang S, Gao Y, Zhou Y.
europepmc   +1 more source

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Curriculum Meta-Learning for Next POI Recommendation

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI ...
Yudong Chen 0003, Xin Wang 0019, Miao Fan, Jizhou Huang, Shengwen Yang, Wenwu Zhu 0001   +5 more
openaire   +1 more source

Next POI Recommendation with Dynamic Graph and Explicit Dependency

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services. Its main objective is to predict the user's next interested POI based on her previous check-in information. Most existing methods directly use users' historical check-in trajectories to construct various graphs to assist sequential models to complete
Feiyu Yin, Yong Liu 0020, Zhiqi Shen 0001, Lisi Chen 0001, Shuo Shang, Peng Han 0005   +5 more
openaire   +1 more source

Embedding-Enhanced Similarity Metrics for Next POI Recommendation

Proceedings of the 12th International Conference on Data Science, Technology and Applications, 2023
Social media platforms allow users to socialize with their peers by sharing information in various forms, including photos and tags. This data, extracted from these online networks, paves the way for innovative research that offers the novel possibility of proposing personalized recommendations, for potential travel destinations based on user-generated
Jarrad, Sara, Naacke, Hubert, Gancarski, Stephane, Gueye, Modou   +3 more
openaire   +3 more sources

Current location-based next POI recommendation

Proceedings of the International Conference on Web Intelligence, 2017
Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours.
Shokirkhon Oppokhonov, Seyoung Park, Isaac K. E. Ampomah   +2 more
openaire   +2 more sources

Next and Next New POI Recommendation via Latent Behavior Pattern Inference

ACM Transactions on Information Systems, 2019
Next and next new point-of-interest (POI) recommendation are essential instruments in promoting customer experiences and business operations related to locations. However, due to the sparsity of the check-in records, they still remain insufficiently studied.
Mingzhong Wang, Liao Lejian
exaly   +4 more sources