Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract)

Moshe Unger; Alexander Tuzhilin

doi:10.1109/ICDE55515.2023.00358

Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract)

Moshe Unger^*, Alexander Tuzhilin

^*Corresponding author for this work

Coller School of Management

New York University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Recommender systems (RSs) have become one of the major applications that aim to tailor items to the user's preferences. Traditional recommendation algorithms capture users' interests and their interactions with items without taking into account contextual information, such as time and location. However, user interests may change depending on the context [1]. In real-life applications, there is plenty of information regarding user's circumstances and surroundings (e.g., the activity of the user, time, location, weather, etc.). Such contextual information can be high-dimensional and is gathered from multiple sources, such as web pages, mobile devices, and more. RSs taking context information into account are called context-aware recommender systems (CARSs) [1].

Original language	English
Title of host publication	Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
Publisher	IEEE Computer Society
Pages	3863-3864
Number of pages	2
ISBN (Electronic)	9798350322279
DOIs	https://doi.org/10.1109/ICDE55515.2023.00358
State	Published - 2023
Event	39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States Duration: 3 Apr 2023 → 7 Apr 2023

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2023-April
ISSN (Print)	1084-4627

Conference

Conference	39th IEEE International Conference on Data Engineering, ICDE 2023
Country/Territory	United States
City	Anaheim
Period	3/04/23 → 7/04/23

Keywords

context
context-aware recommender system
embedding
hierarchical clustering
latent
matrix factorization

Access to Document

10.1109/ICDE55515.2023.00358

Cite this

Unger, M., & Tuzhilin, A. (2023). Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract). In Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023 (pp. 3863-3864). (Proceedings - International Conference on Data Engineering; Vol. 2023-April). IEEE Computer Society. https://doi.org/10.1109/ICDE55515.2023.00358

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abstract = "Recommender systems (RSs) have become one of the major applications that aim to tailor items to the user's preferences. Traditional recommendation algorithms capture users' interests and their interactions with items without taking into account contextual information, such as time and location. However, user interests may change depending on the context [1]. In real-life applications, there is plenty of information regarding user's circumstances and surroundings (e.g., the activity of the user, time, location, weather, etc.). Such contextual information can be high-dimensional and is gathered from multiple sources, such as web pages, mobile devices, and more. RSs taking context information into account are called context-aware recommender systems (CARSs) [1].",

keywords = "context, context-aware recommender system, embedding, hierarchical clustering, latent, matrix factorization",

author = "Moshe Unger and Alexander Tuzhilin",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 39th IEEE International Conference on Data Engineering, ICDE 2023 ; Conference date: 03-04-2023 Through 07-04-2023",

year = "2023",

doi = "10.1109/ICDE55515.2023.00358",

language = "אנגלית",

series = "Proceedings - International Conference on Data Engineering",

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pages = "3863--3864",

booktitle = "Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023",

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Unger, M & Tuzhilin, A 2023, Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract). in Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023. Proceedings - International Conference on Data Engineering, vol. 2023-April, IEEE Computer Society, pp. 3863-3864, 39th IEEE International Conference on Data Engineering, ICDE 2023, Anaheim, United States, 3/04/23. https://doi.org/10.1109/ICDE55515.2023.00358

Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract). / Unger, Moshe; Tuzhilin, Alexander.
Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023. IEEE Computer Society, 2023. p. 3863-3864 (Proceedings - International Conference on Data Engineering; Vol. 2023-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - latent

KW - matrix factorization

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Hierarchical Contextual Embeddings for Context-Aware Recommendations (Extended Abstract)

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Keywords

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