Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Aghiles Salah"'
Autor:
Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espin-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Kücük Mcginty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram Van Den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Ethan Yifan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu
Publikováno v:
Companion Proceedings of the ACM Web Conference 2023.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::841d3da4783be7eeee78aa68e7b58e55
https://doi.org/10.1145/3543873.3587713
https://doi.org/10.1145/3543873.3587713
Autor:
Pablo Montalvo, Aghiles Salah
Publikováno v:
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e41dea4d6b2cc0bbfc9d4a7d9761959
https://doi.org/10.1145/3539597.3575786
https://doi.org/10.1145/3539597.3575786
Publikováno v:
IEEE Internet Computing. 25:50-57
Multimodal recommender systems alleviate the sparsity of historical user–item interactions. They are commonly catalogued based on the type of auxiliary data (modality) they leverage, such as preference data plus user-network (social), user/item tex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9afb88e2ccc4e53b694c3bd1299e6808
https://doi.org/10.1109/mic.2021.3059027
https://doi.org/10.1109/mic.2021.3059027
Publikováno v:
Neurocomputing
Neurocomputing, 2022, 495, pp.105-117. ⟨10.1016/j.neucom.2022.04.122⟩
Neurocomputing, 2022, 495, pp.105-117. ⟨10.1016/j.neucom.2022.04.122⟩
International audience; Non-negative Matrix Factorization (NMF) and its variants have been successfully used for clustering text documents. However, NMF approaches like other models do not explicitly account for the contextual dependencies between wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4098a987d1467e067ee40f15f15cc3e6
https://doi.org/10.1016/j.neucom.2022.04.122
https://doi.org/10.1016/j.neucom.2022.04.122
Publikováno v:
RecSys
Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0aa78150755e63d1fd436bc3d479f685
https://doi.org/10.1145/3460231.3473324
https://doi.org/10.1145/3460231.3473324
Publikováno v:
RecSys
Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9d06ce079093e90826931978e22d78e
https://doi.org/10.1145/3460231.3474265
https://doi.org/10.1145/3460231.3474265
Publikováno v:
WSDM
Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0525d14781e3309158011d8e3bc3cd5e
https://doi.org/10.1145/3437963.3441759
https://doi.org/10.1145/3437963.3441759
Autor:
Mohamed Nadif, Aghiles Salah
Publikováno v:
Advances in Data Analysis and Classification. 13:591-620
Co-clustering addresses the problem of simultaneous clustering of both dimensions of a data matrix. When dealing with high dimensional sparse data, co-clustering turns out to be more beneficial than one-sided clustering even if one is interested in c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::99976e9414b7709bad24df643103d100
https://doi.org/10.1007/s11634-018-0323-4
https://doi.org/10.1007/s11634-018-0323-4
Autor:
Mohammed Nadif, Aghiles Salah
Publikováno v:
Data Mining and Knowledge Discovery. 31:1218-1241
Collaborative filtering (CF) is a widely used technique to guide the users of web applications towards items that might interest them. CF approaches are severely challenged by the characteristics of user-item preference matrices, which are often high
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2db17e12fa87201b6bf4d5295375ee0
https://doi.org/10.1007/s10618-017-0499-9
https://doi.org/10.1007/s10618-017-0499-9
Publikováno v:
Neurocomputing
Neurocomputing, Elsevier, 2016, 175, pp.206--215. 〈http://dx.doi.org/10.1016/j.neucom.2015.10.050〉. 〈10.1016/j.neucom.2015.10.050〉
Neurocomputing, Elsevier, 2016, 175, pp.206--215. ⟨10.1016/j.neucom.2015.10.050⟩
Neurocomputing, Elsevier, 2016, 175, pp.206--215. 〈http://dx.doi.org/10.1016/j.neucom.2015.10.050〉. 〈10.1016/j.neucom.2015.10.050〉
Neurocomputing, Elsevier, 2016, 175, pp.206--215. ⟨10.1016/j.neucom.2015.10.050⟩
International audience; A collaborative filtering system (CF) aims at filtering huge amount of information, in order to guide users of web applications towards items that might interest them. Such a system, consists in recommending a set of personali
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91d92dd952869ba90e2a4ef44c5b3b46
https://doi.org/10.1016/j.neucom.2015.10.050
https://doi.org/10.1016/j.neucom.2015.10.050