Temporally dynamic session-keyword aware sequential recommendation system

Hariram Veeramani*, Surendrabikram Thapa, Usman Naseem

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)

Abstract

Addressing the dynamic preferences and needs of users to provide highly personalized recommendations is a fundamental challenge in recommender systems. To tackle this challenge effectively, understanding both session and keyword information takes on critical significance. Despite the pivotal roles that these two elements play in user interactions, prior research has often approached them in isolation, without a concerted effort to jointly investigate their synergistic potential. To bridge this gap, we propose SeKeBERT4Rec, a novel recommendation model that leverages both session and keyword information within a transformer-based sequential framework. In doing so, we also fill the void between user preferences expressed through keywords and their dynamic behavioral patterns within sessions. Our contributions include introducing a holistic approach to recommendation by seamlessly integrating session and keyword data, conducting an extensive comparative analysis against state-of-the-art methods, and offering in-depth insights through an ablation study that underscores the individual contributions of each model component.

Original languageEnglish
Title of host publication23rd IEEE International Conference on Data Mining Workshops
Subtitle of host publicationproceedings
EditorsJihe Wang, Yi He, Thang N. Dinh, Christan Grant, Meikang Qiu, Witold Pedrycz
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages157-164
Number of pages8
ISBN (Electronic)9798350381641
ISBN (Print)9798350381658
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 - Shanghai, China
Duration: 1 Dec 20234 Dec 2023

Publication series

Name
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
Country/TerritoryChina
CityShanghai
Period1/12/234/12/23

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