11 citations · 45 across the 18 of their papers we have counts for
16 papers · 1 filter
Conversational Recommendation over Live E-Commerce Catalogues with Self-Refreshing Retrieval
Ante Kapetanovic, Tomislav Duricic, Dionizije Fa +2
Conversational recommender systems based on large language models (LLMs) are usually evaluated on static, pre-indexed item collections, yet e-commerce catalogues change continuousl…
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
Tomislav Duricic, Dominik Kowald, Emanuel Lacic +1
By providing personalized suggestions to users, recommender systems have become essential to numerous online platforms. Collaborative filtering, particularly graph-based approaches…
Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings
Emanuel Lacic, Tomislav Duricic, Leon Fadljevic +2
In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements. To…
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
Emanuel Lacic, Dominik Kowald
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Spe…
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Dominik Kowald, Emanuel Lacic
Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative…
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck +2
Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalize…
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
Dominik Kowald, Matthias Traub, Dieter Theiler +5
This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relations…
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric
Emanuel Lacic, Dominik Kowald, Dieter Theiler +4
In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation sys…
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
Markus Reiter-Haas, Emanuel Lacic, Tomislav Duricic +2
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian…
Neighborhood Troubles: On the Value of User Pre-Filtering To Speed Up and Enhance Recommendations
Emanuel Lacic, Dominik Kowald, Elisabeth Lex
In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collaborative Filtering. We propose to pre-filter users i…
AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments
Dominik Kowald, Emanuel Lacic, Dieter Theiler +1
In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide…
Beyond Accuracy Optimization: On the Value of Item Embeddings for Student Job Recommendations
Emanuel Lacic, Dominik Kowald, Markus Reiter-Haas +2
In this work, we address the problem of recommending jobs to university students. For this, we explore the utilization of neural item embeddings for the task of content-based recom…
High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Review
Emanuel Lacic, Dominik Kowald, Elisabeth Lex
Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experienc…
Recommending Items in Social Tagging Systems Using Tag and Time Information
Emanuel Lacic, Dominik Kowald, Paul Seitlinger +2
In this work we present a novel item recommendation approach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time…
SocRecM: A Scalable Social Recommender Engine for Online Marketplaces
Emanuel Lacic, Dominik Kowald, Christoph Trattner
In this paper, we present work-in-progress on SocRecM, a novel social recommendation framework for online marketplaces. We demonstrate that SocRecM is not only easy to integrate wi…
Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces
Emanuel Lacic, Dominik Kowald, Lukas Eberhard +3
Recent research has unveiled the importance of online social networks for improving the quality of recommender systems and encouraged the research community to investigate better w…