Results 281 to 290 of about 208,309 (312)

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Advanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng, Xin Qin, Jiahao Zhang, Wuliang Huang, Yiyang Zhang, Wentao Cui, Yao Chen, Shirui Li, Wenhao Liu, Yao Tian, Yana Liu, Jingxi Dong, Ping Xu, Zhenpeng Man, Guole Liu, Zhongming Liang, Xinlong Jiang, Xiaodong Yang, Pengfei Wang, Ge Yang, Hongmei Wang, Xuezhi Wang, Ming‐Han Tong, Yuanchun Zhou, Shihua Zhang, Yiqiang Chen, Yong Wang, Xin Li   +27 more
wiley   +1 more source

Sistem Rekomendasi Destinasi Wisata di Kota Surabaya Menggunakan Metode Content Based Filtering dan Neural Collaborative Filtering

Zaky Izmi Syakura, Kartika Fithriasari
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Pengembangan Sistem Backend Rekomendasi Produk Dengan Collaborative Filtering Berbasis Singular Value Decomposition

Agustinus Borgy Waluyo, Tifanny Nabarian, Lukman Rosyidi   +2 more
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NEURAL COLLABORATIVE FILTERING BASED GROUP RECOMMENDATIONS

DR.AR. SIVAKUMARAN, YETUKURI GOUTHAMI MAHESWARI, T. Kalyani, TELAKAPALLY RENUSRI   +3 more
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Collaborative Filtering

2011
The interactive tools like blogs, wikis, et cetera, known under the commonly acceptable name Web2.0, led to a new generation of Internet services and applications such as social networks, recommendation systems, reputation systems, et cetera, allowing for public participation in the formation of the content of the Web, and at the same time fueling an ...
Tania Al. Kerkiri, Dimitris Konetas
openaire   +2 more sources

Beyond Collaborative Filtering

Proceedings of the 25th International Conference on World Wide Web, 2016
Most Collaborative Filtering (CF) algorithms are optimized using a dataset of isolated user-item tuples. However, in commercial applications recommended items are usually served as an ordered list of several items and not as isolated items. In this setting, inter-item interactions have an effect on the list's Click-Through Rate (CTR) that is ...
Oren Sar Shalom, Noam Koenigstein, Ulrich Paquet, Hastagiri P. Vanchinathan   +3 more
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Shared collaborative filtering

Proceedings of the fifth ACM conference on Recommender systems, 2011
Traditional collaborative filtering (CF) methods suffer from sparse or even cold-start problems, especially for new established recommenders. However, since there are now quite a few recommender systems already existing in good working order, their data should be valuable to the new-start recommenders. This paper proposes shared collaborative filtering
Yu Zhao, Xinping Feng, Jianqiang Li, Bo Liu   +3 more
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Collaborative competitive filtering

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts
Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, Zhaohui Zheng   +4 more
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Improving Collaborative Filtering

2021 IV International Conference on Control in Technical Systems (CTS), 2021
In this paper, we experiment with a combination of metrics to calculate the similarity when creating collaborative filtering. The Otai Coefficient and Euclidean Distance are used, resulting in a recommender system that produces a satisfactory result.
Jurij A. Morozov, Sergej E. Saradgishvili   +1 more
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Adaptive collaborative filtering

Proceedings of the 2008 ACM conference on Recommender systems, 2008
We present a flexible approach to collaborative filtering which stems from basic research results. The approach is flexible in several dimensions: We introduce an algorithm where the loss can be tailored to a particular recommender problem. This allows us to optimize the prediction quality in a way that matters for the specific recommender system.
Markus Weimer, Alexandros Karatzoglou, Alex Smola   +2 more
openaire   +1 more source