43 citations · 49 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…
Predicting Relevance Scores for Triples from Type-Like Relations using Neural Embedding - The Cabbage Triple Scorer at WSDM Cup 2017
Yael Brumer, Bracha Shapira, Lior Rokach +1
The WSDM Cup 2017 Triple scoring challenge is aimed at calculating and assigning relevance scores for triples from type-like relations. Such scores are a fundamental ingredient for…