Improving Sequential Recommendation via Subsequence Extraction

Deng, Hangyu; Hu, Jinglu

doi:10.1109/ijcnn55064.2022.9892221

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…It can be integrated into the ranking model of a recommender system, delivering refined ranking results for each user query. Many recent advancements have endeavored to the development of various methods that aim to capture users' diverse interests [5][6][7][8][9][10]. One prevalent approach is to employ an attention mechanism to the user behavior sequence and cluster the items into distinct groups, representing different interests or intentions.…”

Section: Introductionmentioning

confidence: 99%

Improving Sequential Recommendation with Global Item Transitions and Local Subsequences

Deng,

2023

IEEJ Transactions Elec Engng

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Many recent sequential recommendation methods have aimed to enhance the modeling of users' diverse interests to provide more accurate and varied recommendations. Despite great success, existing models possess a limitation in their capacity to solely perceive items within a user behavior sequence. Such an approach may potentially constrain their ability to effectively discover users' multiple interests. In this paper, we present a novel approach that addresses this gap by harnessing global item transitions to uncover users' potential interests, subsequently modeling them as local subsequences. To achieve this objective, we represent global item transitions as a directed graph, constructed by amalgamating behavior sequences from the training set. This graph‐based representation allows the design of a specialized module capable of aggregating global item transitional information and uncovering local subsequences specific to individual users. Consequently, downstream sequence models can focus on pertinent item transitions, disregarding unrelated ones. We evaluate the effectiveness of our method on four public data sets, outperforming existing alternatives or delivering competitive results. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Introductionmentioning

confidence: 99%

Improving Sequential Recommendation with Global Item Transitions and Local Subsequences

Deng,

2023

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

Improving Sequential Recommendation via Subsequence Extraction

Cited by 1 publication

References 23 publications

Improving Sequential Recommendation with Global Item Transitions and Local Subsequences

Improving Sequential Recommendation with Global Item Transitions and Local Subsequences

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