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Multimodal Interactive Network for Sequential Recommendation
Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items. Recent work has...
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Transformers for Sequential Recommendation
Sequential recommendation is a recommendation problem that aims to predict the next item in the sequence of user-item interactions. Sequential...
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EMARec: a sequential recommendation with exponential moving average
Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential...
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A general tail item representation enhancement framework for sequential recommendation
Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems (SRS). However, the...
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Channel-Enhanced Contrastive Cross-Domain Sequential Recommendation
Sequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness...
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Sequential recommendation based on multipair contrastive learning with informative augmentation
To solve the recommendation accuracy degradation problem encountered in sequential recommendation cases caused by data sparsity—such as short...
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User view dynamic graph-driven sequential recommendation
In most recommendation scenarios, user information is difficult to obtain due to user privacy and data protection issues. Some graph-based methods...
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Knowledge Graph-Aware Deep Interest Extraction Network on Sequential Recommendation
Sequential recommendation is the mainstream approach in the field of click-through-rate (CTR) prediction for modeling users’ behavior. This behavior...
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Quantifying predictability of sequential recommendation via logical constraints
The sequential recommendation is a compelling technology for predicting users’ next interaction via their historical behaviors. Prior studies have...
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Attenuated sentiment-aware sequential recommendation
Sequential recommendation(SR) focuses on modeling the historical relationship of a user’s behavior. The attention-based models such as Transformer...
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Dynamic time-aware collaborative sequential recommendation with attention-based network
A natural way of user modeling in sequential recommendation is to capture long-term and short-term preferences, respectively, given user historical...
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A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation
E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing...
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HFNF: learning a hybrid Fourier neural filter with a heterogeneous loss for sequential recommendation
Sequential recommendation predicts users’ future interactions by capturing dynamic sequential patterns hidden in their historical behavioral...
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HSA: Hyperbolic Self-attention for Sequential Recommendation
Recently, researchers apply various deep neural networks to the task of sequential recommendation, which captures dynamics of user preference from...
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Long- and short-term collaborative attention networks for sequential recommendation
Sequential recommendation models the users’ historical interaction sequence and predicts which item the user will click next. To better capture the...
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Towards more effective encoders in pre-training for sequential recommendation
Pre-training emerges as a new learning paradigm in natural language processing and computer vision. It has also been introduced into sequential...
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An improved sequential recommendation model based on spatial self-attention mechanism and meta learning
Sequential recommendation systems in cold-start scenarios aim to provide recommendations as accurately as possible for users with sparse behavior,...
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V-BERT4Rec: Enhanced sequential recommendation with multi-modal visual information
The goal of this article is to promote sequential recommender systems to a whole new level of understanding and knowledge integration. We introduce a...
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Knowledge-enhanced personalized hierarchical attention network for sequential recommendation
Sequential recommendation aims to predict the next items that users will interact with according to the sequential dependencies within historical...
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FedMLP4SR: Federated MLP-Based Sequential Recommendation System
Sequential Recommendation predicts users’ next possible item by modeling their historical interaction sequences. Transformer-based models can...