9 papers
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…
Bayesian Hierarchical Words Representation Learning
Oren Barkan, Idan Rejwan, Avi Caciularu +1
This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic…
Paraphrasing vs Coreferring: Two Sides of the Same Coin
Yehudit Meged, Avi Caciularu, Vered Shwartz +1
We study the potential synergy between two different NLP tasks, both confronting predicate lexical variability: identifying predicate paraphrases, and event coreference resolution.…
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…
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Nir Raviv, Avi Caciularu, Tomer Raviv +2
Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum li…