Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Steffen Rendle"'
Autor:
Walid Krichene, Steffen Rendle
Publikováno v:
KDD
Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b40e3f655cc04a20540a62472eff83e9
https://doi.org/10.1145/3535335
https://doi.org/10.1145/3535335
Autor:
Steffen Rendle
Publikováno v:
Recommender Systems Handbook ISBN: 9781071621967
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c97e44482dba6e6da27d450a7755ea18
https://doi.org/10.1007/978-1-0716-2197-4_4
https://doi.org/10.1007/978-1-0716-2197-4_4
Publikováno v:
Recommender Systems Handbook ISBN: 9781071621967
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7acefa8453c63c45f1296e3049fdbe9c
https://doi.org/10.1007/978-1-0716-2197-4_3
https://doi.org/10.1007/978-1-0716-2197-4_3
Autor:
Steffen Rendle, Walid Krichene
Publikováno v:
IJCAI
Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9aeac4a6200d01382a5e480ea651a9c4
https://doi.org/10.24963/ijcai.2021/651
https://doi.org/10.24963/ijcai.2021/651
Publikováno v:
RecSys
Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to combine two or more embeddings, e.g., most notably in matrix factorizatio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1bc7d0dae350a8268dc4eb22b729f741
Autor:
Xiang Ma, Li Zhang, Tao Wu, Heng-Tze Cheng, Ritesh Agarwal, Yu Du, Steffen Rendle, Ankit Kumar, John Anderson, Sarvjeet Singh, Ed H. Chi, Ellie Ka-In Chio, Wen Li, Alex Soares, Pei Cao, Nitin Jindal, Dima Kuzmin, Tushar Deepak Chandra
Publikováno v:
CIKM
Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa815879e4f2196fc8b175ff3dfa5177
Autor:
Steffen Rendle
Publikováno v:
Proceedings of the VLDB Endowment. 6:337-348
The most common approach in predictive modeling is to describe cases with feature vectors (aka design matrix). Many machine learning methods such as linear regression or support vector machines rely on this representation. However, when the underlyin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0990c3bf505db62929363a2e79790427
https://doi.org/10.14778/2535573.2488340
https://doi.org/10.14778/2535573.2488340
Publikováno v:
WWW
In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a5471f70dd80808d8feee393009b2e5
http://arxiv.org/abs/1611.04666
http://arxiv.org/abs/1611.04666
Publikováno v:
KDD
The convergence behavior of many distributed machine learning (ML) algorithms can be sensitive to the number of machines being used or to changes in the computing environment. As a result, scaling to a large number of machines can be challenging. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c745765956fcd1d637754fea6216381
https://doi.org/10.1145/2939672.2939790
https://doi.org/10.1145/2939672.2939790
Autor:
Steffen Rendle
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 3:1-22
Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd2a22515a1cee506a84df5b0d21037c
https://doi.org/10.1145/2168752.2168771
https://doi.org/10.1145/2168752.2168771