Multi-Behavior RFM Model Based on Improved SOM Neural Network Algorithm for Customer Segmentation
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DOI: 10.1109/access.2022.3223361
Abstract: Previous research on RFM (recency, frequency, and monetary value) models focused on only one type of user behavior data, i.e., the purchase behavior, without considering the interactions between users and items, such as clicking, favorite,… read more here.
Keywords: multi behavior; rfm; behavior rfm; customer segmentation ... See more keywords
Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional Networks
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DOI: 10.1109/access.2023.3251994
Abstract: Traditional recommendation systems usually use single user-item interaction information, which ignore the multiple relationships that exist for other interactions (e.g., likes, clicks). Multi-behavioral recommendation models compensate for the shortcomings of traditional models. The existing multi-behavior… read more here.
Keywords: information; interaction; multi behavior; recommendation ... See more keywords
Multi-Behavior Hypergraph Contrastive Learning for Session-Based Recommendation
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DOI: 10.1109/tkde.2024.3523383
Abstract: Most current session-based recommendations model session sequences solely based on the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors. Additionally, they use ordinary graphs to model one-to-one item correlations in the current… read more here.
Keywords: session representation; multi behavior; session; contrastive learning ... See more keywords
Implicit Multi-Behavior Generative Recommendation With Mixture of Quantization
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DOI: 10.1109/tkde.2025.3572014
Abstract: Generative recommendation systems have recently seen a surge in interest, largely due to the promising advancements in generative AI. As a competitive solution for multi-behavior sequence recommendations, much of the recent research has concentrated on… read more here.
Keywords: mixture quantization; quantization; multi behavior; behavior ... See more keywords
Accurate multi-behavior sequence-aware recommendation via graph convolution networks
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DOI: 10.1371/journal.pone.0314282
Abstract: How can we recommend items to users utilizing multiple types of user behavior data? Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. These systems aim… read more here.
Keywords: aware recommendation; sequence aware; multi behavior; recommendation ... See more keywords