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2020 – today
- 2024
[c34]Zhouhang Xie
, Junda Wu, Hyunsik Jeon, Zhankui He, Harald Steck, Rahul Jha, Dawen Liang, Nathan Kallus, Julian J. McAuley:
Neighborhood-Based Collaborative Filtering for Conversational Recommendation. RecSys 2024: 1045-1050- [c33]Harald Steck, Chaitanya Ekanadham, Nathan Kallus:
Is Cosine-Similarity of Embeddings Really About Similarity? WWW (Companion Volume) 2024: 887-890 - [i11]Harald Steck, Chaitanya Ekanadham, Nathan Kallus:
Is Cosine-Similarity of Embeddings Really About Similarity? CoRR abs/2403.05440 (2024) - [i10]Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian J. McAuley:
Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. CoRR abs/2405.12119 (2024) - 2023
- [c32]Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley:
Large Language Models as Zero-Shot Conversational Recommenders. CIKM 2023: 720-730 - [i9]Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley:
Large Language Models as Zero-Shot Conversational Recommenders. CoRR abs/2308.10053 (2023) - 2021
- [j4]Harald Steck, Linas Baltrunas, Ehtsham Elahi, Dawen Liang, Yves Raimond, Justin Basilico:
Deep Learning for Recommender Systems: A Netflix Case Study. AI Mag. 42(3): 7-18 (2021) - [c31]Harald Steck, Dawen Liang:
Negative Interactions for Improved Collaborative Filtering: Don't go Deeper, go Higher. RecSys 2021: 34-43 - [i8]Harald Steck, Dario García-García:
On the Regularization of Autoencoders. CoRR abs/2110.11402 (2021) - 2020
- [c30]Harald Steck:
Autoencoders that don't overfit towards the Identity. NeurIPS 2020 - [c29]Harald Steck, Maria Dimakopoulou, Nickolai Riabov, Tony Jebara:
ADMM SLIM: Sparse Recommendations for Many Users. WSDM 2020: 555-563
2010 – 2019
- 2019
- [c28]Harald Steck:
Markov Random Fields for Collaborative Filtering. NeurIPS 2019: 5474-5485 - [c27]Harald Steck:
Embarrassingly Shallow Autoencoders for Sparse Data. WWW 2019: 3251-3257 - [i7]Harald Steck:
Collaborative Filtering via High-Dimensional Regression. CoRR abs/1904.13033 (2019) - [i6]Harald Steck:
Embarrassingly Shallow Autoencoders for Sparse Data. CoRR abs/1905.03375 (2019) - [i5]Harald Steck:
Markov Random Fields for Collaborative Filtering. CoRR abs/1910.09645 (2019) - 2018
- [c26]Harald Steck:
Calibrated recommendations. RecSys 2018: 154-162 - 2015
- [c25]Harald Steck:
Gaussian Ranking by Matrix Factorization. RecSys 2015: 115-122 - [c24]Harald Steck, Roelof van Zwol, Chris Johnson:
Interactive Recommender Systems: Tutorial. RecSys 2015: 359-360 - 2014
- [j3]Xiwang Yang, Yang Guo, Yong Liu, Harald Steck:
A survey of collaborative filtering based social recommender systems. Comput. Commun. 41: 1-10 (2014) - [c23]Panagiotis Adamopoulos, Alejandro Bellogín, Pablo Castells, Paolo Cremonesi, Harald Steck:
REDD 2014 - international workshop on recommender systems evaluation: dimensions and design. RecSys 2014: 393-394 - 2013
- [c22]Harald Steck:
Evaluation of recommendations: rating-prediction and ranking. RecSys 2013: 213-220 - [i4]Harald Steck, Tommi S. Jaakkola:
Unsupervised Active Learning in Large Domains. CoRR abs/1301.0602 (2013) - [i3]Harald Steck:
On the Use of Skeletons when Learning in Bayesian Networks. CoRR abs/1301.3894 (2013) - 2012
- [j2]Tin Kam Ho, Kim Matthews, Lawrence O'Gorman, Harald Steck:
Public Space Behavior Modeling With Video and Sensor Analytics. Bell Labs Tech. J. 16(4): 203-217 (2012) - [j1]Piotr Mirowski, Philip Whiting, Harald Steck, Ravishankar Palaniappan, Michael MacDonald, Detlef Hartmann, Tin Kam Ho:
Probability kernel regression for WiFi localisation. J. Locat. Based Serv. 6(2): 81-100 (2012) - [c21]Xiwang Yang, Harald Steck, Yong Liu:
Circle-based recommendation in online social networks. KDD 2012: 1267-1275 - [c20]Xiwang Yang, Harald Steck, Yang Guo, Yong Liu:
On top-k recommendation using social networks. RecSys 2012: 67-74 - [e1]Xavier Amatriain, Pablo Castells, Arjen P. de Vries, Christian Posse, Harald Steck:
Proceedings of the Workshop on Recommendation Utility Evaluation: Beyond RMSE, RUE 2012, Dublin, Ireland, September 9, 2012. CEUR Workshop Proceedings 910, CEUR-WS.org 2012 [contents] - [i2]Harald Steck:
Learning the Bayesian Network Structure: Dirichlet Prior versus Data. CoRR abs/1206.3287 (2012) - [i1]Harald Steck:
Ranking by Dependence - A Fair Criteria. CoRR abs/1206.6871 (2012) - 2011
- [c19]Piotr Mirowski, Harald Steck, Philip Whiting, Ravishankar Palaniappan, Michael MacDonald, Tin Kam Ho:
KL-divergence kernel regression for non-Gaussian fingerprint based localization. IPIN 2011: 1-10 - [c18]Harald Steck:
Item popularity and recommendation accuracy. RecSys 2011: 125-132 - [c17]Yu Xin, Harald Steck:
Multi-value probabilistic matrix factorization for IP-TV recommendations. RecSys 2011: 221-228 - 2010
- [c16]Harald Steck:
Training and testing of recommender systems on data missing not at random. KDD 2010: 713-722 - [c15]Harald Steck, Yu Xin:
A Generalized Probabilistic Framework and its Variants for Training Top-k Recommender System. PRSAT@RecSys 2010: 35-42
2000 – 2009
- 2009
- [c14]Marianne Mueller, Rómer Rosales, Harald Steck, Sriram Krishnan, Bharat Rao, Stefan Kramer:
Data-Efficient Information-Theoretic Test Selection. AIME 2009: 410-415 - [c13]Marianne Mueller, Rómer Rosales, Harald Steck, Sriram Krishnan, Bharat Rao, Stefan Kramer:
Subgroup Discovery for Test Selection: A Novel Approach and Its Application to Breast Cancer Diagnosis. IDA 2009: 119-130 - 2008
- [c12]Harald Steck:
Learning the Bayesian Network Structure: Dirichlet Prior vs Data. UAI 2008: 511-518 - 2007
- [c11]Harald Steck:
Hinge Rank Loss and the Area Under the ROC Curve. ECML 2007: 347-358 - [c10]Maleeha Qazi, Glenn Fung, Sriram Krishnan, Rómer Rosales, Harald Steck, R. Bharat Rao, Don Poldermans, Dhanalakshmi Chandrasekaran:
Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks. IJCAI 2007: 519-525 - [c9]Vikas C. Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin:
On Ranking in Survival Analysis: Bounds on the Concordance Index. NIPS 2007: 1209-1216 - [c8]Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, Harald Steck, R. Bharat Rao:
Bayesian Co-Training. NIPS 2007: 1665-1672 - [c7]Harald Steck, Tommi S. Jaakkola:
Predictive Discretization during Model Selection. AISTATS 2007: 532-539 - 2006
- [c6]Harald Steck:
Ranking by Dependence - A Fair Criteria. UAI 2006 - 2004
- [c5]Harald Steck, Tommi S. Jaakkola:
Predictive Discretization During Model Selection. DAGM-Symposium 2004: 1-8 - 2003
- [c4]Harald Steck, Tommi S. Jaakkola:
Bias-Corrected Bootstrap and Model Uncertainty. NIPS 2003: 521-528 - 2002
- [c3]Harald Steck, Tommi S. Jaakkola:
On the Dirichlet Prior and Bayesian Regularization. NIPS 2002: 697-704 - [c2]Harald Steck, Tommi S. Jaakkola:
Unsupervised Active Learning in Large Domains. UAI 2002: 469-476 - 2001
- [b1]Harald Steck:
Constraint-based structural learning in Bayesian networks using finite data sets. Technical University Munich, Germany, 2001, pp. 1-171 - 2000
- [c1]Harald Steck:
On the Use of Skeletons when Learning in Bayesian Networks. UAI 2000: 558-565
Coauthor Index
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