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CitRec 2017
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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The "International Workshop on Recommender Systems for Citizens" (CitRec) is focused on a novel type of recommender systems both in terms of ownership and purpose: recommender systems run by citizens and ...
Acknowledgments We thank the Social Urban Data Lab (SUDL) of the Amsterdam Institute for Advanced Metropolitan Solutions (AMS) and Delft Data Science (DDS) for supporting the workshop. ...
Thus they describe comprehensively citizens' behaviors and their relationships with the environment, providing opportunities for RecSys '17, August 27-31, 2017, Como, Italy Jie Yang, Zhu Sun, Alessandro ...
doi:10.1145/3109859.3109969
dblp:conf/recsys/YangSBZL17
fatcat:w7glpy4gmjfmvn6eccvl53i4ni
Folding
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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In recommender systems based on low-rank factorization of a partially observed user-item matrix, a common phenomenon that plagues many otherwise e ective models is the interleaving of good and spurious ...
A single spurious recommendation can dramatically impact the perceived quality of a recommender system. ...
Algorithms II RecSys'17, August 27-31, 2017, Como, Italy In a typical recommender system powered by low-rank matrix factorization, the standard method for generating recommendations, after the embedding ...
doi:10.1145/3109859.3109911
dblp:conf/recsys/XinMPLA17
fatcat:yqbyemdj5vbmpdhwu5kp6hywvu
RecSys'17 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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This finding has the potential to improve the efficiency of data collection for applications such as Top-N recommender systems; where we are primarily interested in the ranked order of items, rather than ...
We hypothesize that the issues arising from rater bias may be mitigated by treating the data received as an ordered set of preferences rather than a collection of absolute values. ...
Researchers found that many respondents can be categorised as displaying one or more of these Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, August 27, 2017, Como, Italy ...
doi:10.1145/3109859.3109961
dblp:conf/recsys/BrusilovskyGFLO17
fatcat:vishcvo5jrdbnnj24ncydrbcfm
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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2017 pre-print arXiv:1706.04148v4 fatcat:f53p35v4tbefbm3g5ajl3bnxbmOther Versions
Results on two industry datasets show large improvements over the session-only RNNs. ...
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. ...
RecSys'17, August 27-31, 2017, Como, Italy. © 2017 ACM. 978-1-4503-4652-8/17/08. . . $15.00 DOI: http://dx.doi.org/10.1145/3109859.3109896
Formally, for each session S m = {i m,1 , i m,2 , ..., i m, ...
doi:10.1145/3109859.3109896
dblp:conf/recsys/QuadranaKHC17
fatcat:zlizm2zazjgilgojsfunlb7rwm
Metalearning for Context-aware Filtering
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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on new problems is the development of useful measures able to characterize the data, i.e. metafeatures. ...
is work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. e most important challenge of applying metalearning ...
ACKNOWLEDGEMENTS is work is nanced by the ERDF Fund through the Operational Program for Competitiveness and Internationalization -COMPETE 2020 of Portugal 2020 through the National Innovation Agency (ANI ...
doi:10.1145/3109859.3109899
dblp:conf/recsys/0001SC17
fatcat:mt5ybgongrhv3fjhxmusukoidi