1 citations · 2 across the 7 of their papers we have counts for
7 papers
SwapRec: Warming Up Cold Items Through Training-Time Swaps
Marta Moscati, Jan Malte Lichtenberg, Davide Abbattista +3
Interactions with cold items negatively impact real-time personalization of ID-based recommender systems. This is because the use of such interactions degrades user preference esti…
DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation
Jan Malte Lichtenberg, Antonio De Candia, Matteo Ruffini
Transformer-based sequential recommenders, such as SASRec or BERT4Rec, typically rely solely on learned item ID embeddings, making them vulnerable to the item cold-start problem, p…
Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending
Jan Malte Lichtenberg, Giuseppe Di Benedetto, Matteo Ruffini
An increasing number of media streaming services have expanded their offerings to include entities of multiple content types. For instance, audio streaming services that started by…
Large Language Models as Recommender Systems: A Study of Popularity Bias
Jan Malte Lichtenberg, Alexander Buchholz, Pola Schwöbel
The issue of popularity bias -- where popular items are disproportionately recommended, overshadowing less popular but potentially relevant items -- remains a significant challenge…
Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation
Jan Malte Lichtenberg, Alexander Buchholz, Giuseppe Di Benedetto +2
"Clipping" (a.k.a. importance weight truncation) is a widely used variance-reduction technique for counterfactual off-policy estimators. Like other variance-reduction techniques, c…
Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling
Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto +3
The Plackett-Luce (PL) model is ubiquitous in learning-to-rank (LTR) because it provides a useful and intuitive probabilistic model for sampling ranked lists. Counterfactual offlin…
Iterative Policy-Space Expansion in Reinforcement Learning
Jan Malte Lichtenberg, Özgür Şimşek
Humans and animals solve a difficult problem much more easily when they are presented with a sequence of problems that starts simple and slowly increases in difficulty. We explore…