- Multimodal Implicit Feedback for Recommender Systems2017 | Faculty of Mathematics and Physics
- Negative implicit feedback in e-commerce recommender systems2013 | Faculty of Mathematics and Physics
- Towards Complex User Feedback and Presentation Context in Recommender Systems2017 | Faculty of Mathematics and Physics
- Modelling User Preferences from Implicit Preference Indicators via Compensational Aggregations2014 | Faculty of Mathematics and Physics
- Recommending for Disloyal Customers with Low Consumption Rate2014 | Faculty of Mathematics and Physics
- Using Implicit Preference Relations to Improve Recommender Systems2017 | Faculty of Mathematics and Physics
- IPIget: The Component for Collecting Implicit User Preference Indicators2014 | Faculty of Mathematics and Physics
- User Feedback and Preferences Mining2012 | Faculty of Mathematics and Physics
- Interpreting Web Shop User's Behavioral Patterns as Fictitious Explicit Rating for Preference Learning2014 | Faculty of Mathematics and Physics
- Using the Context of User Feedback in Recommender Systems2016 | Faculty of Mathematics and Physics
- Estimating importance of implicit factors in E-commerce recommender systems2013 | Faculty of Mathematics and Physics
- Using Implicit Preference Relations to Improve Content Based Recommending2015 | Faculty of Mathematics and Physics
- The Effect of Feedback Granularity on Recommender Systems Performance2022 | Faculty of Mathematics and Physics
- Rank-sensitive proportional aggregations in dynamic recommendation scenarios2022 | Faculty of Mathematics and Physics