Emanuel Lacic
Emanuel Lacic
Principal Engineer @ Infobip
Verified email at - Homepage
Cited by
Cited by
High Enough? explaining and predicting traveler Satisfaction using airline reviews
E Lacic, D Kowald, E Lex
Proceedings of the 27th ACM Conference on Hypertext and Social Media, 249-254, 2016
Trust-based collaborative filtering: Tackling the cold start problem using regular equivalence
T Duricic, E Lacic, D Kowald, E Lex
Proceedings of the 12th ACM conference on recommender systems, 446-450, 2018
Tagrec: Towards a standardized tag recommender benchmarking framework
D Kowald, E Lacic, C Trattner
Proceedings of the 25th ACM conference on Hypertext and social media, 305-307, 2014
Tackling Cold-Start Users in Recommender Systems with Indoor Positioning Systems.
E Lacic, D Kowald, M Traub, G Luzhnica, J Simon, E Lex
RecSys Posters, 2015
Recommending items in social tagging systems using tag and time information
E Lacic, D Kowald, P Seitlinger, C Trattner, D Parra
arXiv preprint arXiv:1406.7727, 2014
Towards a scalable social recommender engine for online marketplaces: The case of apache solr
E Lacic, D Kowald, D Parra, M Kahr, C Trattner
Proceedings of the 23rd International Conference on World Wide Web, 817-822, 2014
Using autoencoders for session-based job recommendations
E Lacic, M Reiter-Haas, D Kowald, M Reddy Dareddy, J Cho, E Lex
User Modeling and User-Adapted Interaction 30, 617-658, 2020
Utilizing online social network and location-based data to recommend products and categories in online marketplaces
E Lacic, D Kowald, L Eberhard, C Trattner, D Parra, LB Marinho
Mining, Modeling, and Recommending'Things' in Social Media: 4th†…, 2015
Should we embed in chemistry? A comparison of unsupervised transfer learning with PCA, UMAP, and VAE on molecular fingerprints
M Lovrić, T Đuričić, HTN Tran, H Hussain, E Lacić, MA Rasmussen, ...
Pharmaceuticals 14 (8), 758, 2021
Should we embed? A study on the online performance of utilizing embeddings for real-time job recommendations
E Lacic, M Reiter-Haas, T Duricic, V Slawicek, E Lex
Proceedings of the 13th ACM Conference on Recommender Systems, 496-500, 2019
Popularity bias in collaborative filtering-based multimedia recommender systems
D Kowald, E Lacic
International Workshop on Algorithmic Bias in Search and Recommendation, 1-11, 2022
Predicting treatment outcomes using explainable machine learning in children with asthma
M Lovrić, I Banić, E Lacić, K Pavlović, R Kern, M Turkalj
Children 8 (5), 376, 2021
TagRec: towards a toolkit for reproducible evaluation and development of tag-based recommender algorithms
C Trattner, D Kowald, E Lacic
ACM SIGWEB Newsletter 2015 (Winter), 1-10, 2015
Scar: Towards a real-time recommender framework following the microservices architecture
E Lacic, M Traub, D Kowald, E Lex
Proceedings of the Workshop on Large Scale Recommender Systems (LSRS2015) at†…, 2015
The social semantic server: a flexible framework to support informal learning at the workplace
S Dennerlein, D Kowald, E Lex, D Theiler, E Lacic, T Ley
Proceedings of the 15th International Conference on Knowledge Technologies†…, 2015
Beyond accuracy optimization: On the value of item embeddings for student job recommendations
E Lacic, D Kowald, M Reiter-Haas, V Slawicek, E Lex
arXiv preprint arXiv:1711.07762, 2017
Socrecm: A scalable social recommender engine for online marketplaces
E Lacic, D Kowald, C Trattner
Proceedings of the 25th ACM conference on Hypertext and social media, 308-310, 2014
AFEL-REC: a recommender system for providing learning resource recommendations in social learning environments
D Kowald, E Lacic, D Theiler, E Lex
arXiv preprint arXiv:1808.04603, 2018
LIM app: Reflecting on audience feedback for improving presentation skills
V Rivera-Pelayo, E Lacić, V Zacharias, R Studer
Scaling up Learning for Sustained Impact: 8th European Conference, on†…, 2013
What drives readership? an online study on user interface types and popularity bias mitigation in news article recommendations
E Lacic, L Fadljevic, F Weissenboeck, S Lindstaedt, D Kowald
European Conference on Information Retrieval, 172-179, 2022
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