Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Youfang Leng"'
Autor:
Youfang Leng, Li Yu
Publikováno v:
IEEE Access, Vol 9, Pp 51618-51630 (2021)
Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers’ interactive behaviors. The user’s behavior in the session not only contains the item sequence but also rich context information suc
Externí odkaz:
https://doaj.org/article/29cb7516c171404897fba39701678320
Publikováno v:
Information Sciences. 622:115-132
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc5b4bd64d08b68792800db09a0c0c19
https://doi.org/10.1016/j.ins.2022.11.158
https://doi.org/10.1016/j.ins.2022.11.158
Publikováno v:
Information Sciences. 614:223-239
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::48e46d2cd5b311ca0521575aea368ce6
https://doi.org/10.1016/j.ins.2022.09.058
https://doi.org/10.1016/j.ins.2022.09.058
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Jan2024, Vol. 18 Issue 1, p1-28, 28p
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d61c4327b6d2a01e8a6460a2fe1b46a6
https://doi.org/10.1109/icassp43922.2022.9747757
https://doi.org/10.1109/icassp43922.2022.9747757
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cec7d31bafc9d320db32b58328c807f7
https://doi.org/10.1109/icassp43922.2022.9747580
https://doi.org/10.1109/icassp43922.2022.9747580
Publikováno v:
Decision Support Systems. 165:113894
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::148a1f1693654723f13014194fa601fd
https://doi.org/10.1016/j.dss.2022.113894
https://doi.org/10.1016/j.dss.2022.113894
Publikováno v:
ICASSP
Cross-domain recommendation (CDR) technology is proved to be an effective way to tackle the difficulties encountered by traditional recommender technology (e.g. CF), such as data sparsity and cold-start. However, on account of the heterogeneity, it i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e4c8abb32223b0d5d9917b8ef4c966c0
https://doi.org/10.1109/icassp39728.2021.9413924
https://doi.org/10.1109/icassp39728.2021.9413924
Autor:
Youfang Leng, Li Yu
Publikováno v:
Pattern Recognition. 127:108601
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d7b58bf84ae4ad824d5070350be987a
https://doi.org/10.1016/j.patcog.2022.108601
https://doi.org/10.1016/j.patcog.2022.108601
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783030594183
DASFAA (3)
DASFAA (3)
Next-basket recommendation plays an important role in both online and offline market. Existing methods often suffer from three challenges: information loss in basket encoding, sequential pattern mining of the shopping history, and the diversity of re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5cade4e5fe353ecfe3a2f01a7c701187
https://doi.org/10.1007/978-3-030-59419-0_39
https://doi.org/10.1007/978-3-030-59419-0_39
