124 citations · 209 across the 31 of their papers we have counts for
8 papers · 1 filter
Why This, Not That? Mining User Profiles for Pair-wise Counterfactuals
Meysam Varasteh, Veronika Bogina, Noam Koenigstein +1
The topic of explanation in recommender systems has seen steady research attention since the earliest days of the field. With some exceptions, this work has focused on the explanat…
Fidelity-Aware Recommendation Explanations via Stochastic Path Integration
Oren Barkan, Yahlly Schein, Yehonatan Elisha +3
Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems. We introduce SPINRec…
Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems
Dor Arviv, Yehonatan Elisha, Oren Barkan +1
We present a method for extracting \emph{monosemantic} neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in r…
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates
Oren Barkan, Roy Hirsch, Ori Katz +3
A major challenge in collaborative filtering methods is how to produce recommendations for cold items (items with no ratings), or integrate cold item into an existing catalog. Over…
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
Oren Barkan, Yonatan Fuchs, Avi Caciularu +1
Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Mu…
RecoBERT: A Catalog Language Model for Text-Based Recommendations
Itzik Malkiel, Oren Barkan, Avi Caciularu +3
Language models that utilize extensive self-supervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety…
Attentive Item2Vec: Neural Attentive User Representations
Oren Barkan, Avi Caciularu, Ori Katz +1
Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendatio…
CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations
Oren Barkan, Noam Koenigstein, Eylon Yogev +1
In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB). CF algorithms are trained using a dataset of user…