Alexander Tuzhilin

About

Alexander Tuzhilin is a faculty member associated with the Learning Science Lab at NYU Stern. The lab is a team of creatives, educators, designers, and technologists who collaborate with faculty to build meaningful learning environments and create engaging, interactive courses to advance business school education. Tuzhilin's work involves partnering with faculty to develop innovative learning experiences, integrating technology into teaching, and enhancing educational methods through research and practical applications. His contributions support the lab's mission to improve learning outcomes by leveraging technology and design principles in business education.

Research topics

• Artificial Intelligence
• Machine Learning
• Information Retrieval
• Computer Science
• Data Mining
• Data science
• Mathematics

Selected publications

• DDTCDR

  2020 · 260 citations

  Senior authorCorresponding

  Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cross-domain models did not take into account bidirectional latent relations between users and items. In addition, they do not explicitly model information of user and item features, while utilizing only user ratings information for recommendations. To address these concerns, in this paper we propose a novel approach to…

  DOI

• Context-Aware Recommendations Based on Deep Learning Frameworks

  ACM Transactions on Management Information Systems · 2020 · 86 citations

  In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based…

  DOI

• Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations

  IEEE Transactions on Knowledge and Data Engineering · 2021 · 67 citations

  Senior authorCorresponding

  Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. In addition, they did not consider the duality structure of cross-domain recommendation tasks, thus failing to take into account bidirectional latent relations between users and items and achieve optimal recommendation per…

  DOI

• Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

  Information Systems Research · 2021-12-15 · 29 citations

  articleSenior author

  In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second,…

  PublisherDOI

• CoLES: Contrastive Learning for Event Sequences with Self-Supervision

  Proceedings of the 2022 International Conference on Management of Data · 2022-06-10 · 24 citations

  articleOpen accessSenior author

  We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw data in low-dimensional fixed-length vector representations that could be easily applied in various downstream machine learning tasks. In this paper, we propose a new method "CoLES", which adapts contrastive learning, previously used for audio and computer vision domains, to the discrete event sequences domain in a se…

  PublisherOA PDFDOI

Recent grants

• EAGER: Collaborative Research: Sequential Recommender Systems in Mobile and Pervasive Environments

  NSF · $75k · 2012–2014

Frequent coauthors

• Gediminas Adomavičius

  49 shared

• Konstantin Bauman

  Temple University

  20 shared

• Moshe Unger

  College of Management Academic Studies

  16 shared

• Bamshad Mobasher

  16 shared

• Michele Gorgoglione

  Polytechnic University of Bari

  14 shared

• James Clifford

  14 shared

• Pan Li

  Case Western Reserve University

  13 shared

• Balaji Padmanabhan

  13 shared

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