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Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling [article]

Jiarui Qin, Kan Ren, Yuchen Fang, Weinan Zhang, Yong Yu
2019 pre-print
To tackle the problems of the current sequential recommendation models, we propose Sequential Collaborative Recommender (SCoRe) which effectively mines high-order collaborative information using cross-neighbor  ...  Without modeling collaborative relations, those methods suffer from the lack of recommendation diversity and thus may have worse performance.  ...  The third group is dual sequence recommendation models. RRN [36] is the first RNN-based model that considers both the user-and item-side sequence.  ... 
doi:10.1145/3336191.3371842 arXiv:1911.03883v1 fatcat:nls7jk24bnhejcwmgvpwaqd2z4

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the  ...  ; and 3) temporal/sequential recommendation, which accounts for the contextual information associated with an interaction, such as time, location, and the past interactions.  ...  Besides, as both users and items could be associated with content information, dual autoencoder based recommendation models have been proposed [30] , [54] , [73] , [81] .  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation [article]

Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao
2022 arXiv   pre-print
However, existing propagation-based methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality  ...  A recent technical trend is to design end-to-end models based on information propagation schemes.  ...  RELATED WORK Existing recommendation systems incorporated with KG information can be mainly categorized into three clusters, viz., embeddingbased methods, path-based methods, and propagation-based methods  ... 
arXiv:2204.04959v1 fatcat:xjj3a7e2z5dyxfaim24m24hnha

Multi-Faceted Global Item Relation Learning for Session-Based Recommendation

Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song, Li Li
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
As an emerging paradigm, session-based recommendation is aimed at recommending the next item based on a set of anonymous sessions.  ...  Specifically, we show that learning negative relations is critical for session-based recommendation.  ...  RELATED WORK Since session-based recommendation can be seen as a special case of sequential recommendation, in this section we review both sequential recommendation models and session-based recommendation  ... 
doi:10.1145/3477495.3532024 fatcat:4xn34fyl5nc53jhoxplsa7nmju

HAKG

Yuntao Du, Xinjun Zhu, Lu Chen, Baihua Zheng, Yunjun Gao
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
A recent technical trend is to design end-to-end models based on the information propagation schemes.  ...  However, existing propagationbased methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality  ...  RELATED WORK Existing recommendation systems incorporated with KG information can be mainly categorized into three clusters, viz., embeddingbased methods, path-based methods, and propagation-based methods  ... 
doi:10.1145/3477495.3531987 fatcat:drb4k3f3ufczdawlcys3pouiva

Double Attention Convolutional Neural Network for Sequential Recommendation

Qi Chen, Guohui Li, Quan Zhou, Si Shi, Deqing Zou
2022 ACM Transactions on the Web  
Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence.  ...  Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics.  ...  Some sequential recommendation models that have tried combining user-side and item-side sequences to perform dual sequence modeling, such as [20, 32, 47ś49 ].  ... 
doi:10.1145/3555350 fatcat:ougoeomzpnhxrpp5udhhcr3iby

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
We focus on the work based on deep learning techniques, an emerging area in the recommendation research.  ...  A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  Compared with memory-based methods, which uses trust ties to infer users' neighbors and then promote the accuracy of similarity calculation among users, most model-based methods simultaneously map users  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

Geometric Interaction Augmented Graph Collaborative Filtering [article]

Yiding Zhang, Chaozhuo Li, Senzhang Wang, Jianxun Lian, Xing Xie
2022 arXiv   pre-print
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.  ...  However, the interaction graphs inherently exhibit the hybrid and nested geometric characteristics, while the existing single geometry-based models are inadequate to fully capture such sophisticated topological  ...  However, by enjoying the merits from both Euclidean and hyperbolic sides, the performance of our proposal surpasses the single space-based models.  ... 
arXiv:2208.01250v1 fatcat:kkiyhbs6cfgonjrp65me7pn5by

Mixed Information Flow for Cross-domain Sequential Recommendations [article]

Muyang Ma and Pengjie Ren and Zhumin Chen and Zhaochun Ren and Lifan Zhao and Jun Ma and Maarten de Rijke
2020 arXiv   pre-print
Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains.  ...  One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.  ...  A collaborative session-based recommendation approach with parallel memory modules.  ... 
arXiv:2012.00485v3 fatcat:kl4klnly75aodjudrprio6i4cm

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation [article]

Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang
2021 arXiv   pre-print
exhibited with temporally-ordered and multi-level inter-dependent relation structures.  ...  However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing the complex transition dynamics  ...  By integrating a positionaware dual-stage attention network and graph hierarchical relation encoder, MTD not only models the intra-session sequential transitions, but also derives the high-order item relationships  ... 
arXiv:2110.03996v1 fatcat:qp5o3osmofgttnnas7r6b6lowu

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

Lianghao Xia, Chao Huang, Yong Xu, Jian Pei
2022 IEEE Transactions on Knowledge and Data Engineering  
Modeling time-evolving preferences of users with their sequential item interactions, has attracted increasing attention in many online applications.  ...  The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.  ...  RELATED WORK In this section, we summarize the relevant research work from the following research lines: i.e., i) sequential recommendation, ii) graph-based recommender systems, iii) recommendation with  ... 
doi:10.1109/tkde.2022.3175094 fatcat:iqreqptfvbeeffmit4isv7xsuu

Knowledge-aware Coupled Graph Neural Network for Social Recommendation [article]

Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye
2021 arXiv   pre-print
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider  ...  Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering.  ...  Social Recommendation with Sequential Pattern. • DGRec (Song et al. 2019): it jointly models the dynamic user's preference and the underlying social relations. Knowledge Graph-enhanced Recommendation.  ... 
arXiv:2110.03987v1 fatcat:ra6xspadufe5dfjgntb5mfxlli

Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation [article]

Lei Guo, Jinyu Zhang, Li Tang, Tong Chen, Lei Zhu, Hongzhi Yin
2022 arXiv   pre-print
Existing works on SCSR mainly rely on mining sequential patterns via Recurrent Neural Network (RNN)-based models, which suffer from the following limitations: 1) RNN-based methods overwhelmingly target  ...  Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.  ...  It solves a parallel sequential recommendation problem with a Gated Recurrent Unit (GRU)-based information-sharing network. Another related work is the PSJNet method [6] .  ... 
arXiv:2206.08050v1 fatcat:qxxurwlepjb2pm3fxgqmz6pbma

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation [article]

Junfa Lin, Siyuan Chen, Jiahai Wang
2022 arXiv   pre-print
Recommender systems based on graph neural networks receive increasing research interest due to their excellent ability to learn a variety of side information including social networks.  ...  of users and items and incorporates their relational influence.  ...  [29] proposed a dual graph attention network to collaboratively learn representations for twofold social effects. Song et al.  ... 
arXiv:2201.10751v2 fatcat:rpwxl55lmzghfe7jaxdq6avxxy

IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search [article]

Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He
2022 arXiv   pre-print
IHGNN resorts to a hypergraph constructed from the historical user-product-query interactions, which could completely preserve ternary relations and express collaborative signal based on the topological  ...  It collects the information from the hypergraph neighbors and explicitly models neighbor feature interaction to enhance the representation of the target entity.  ...  [35] developed a next-item recommendation framework empowered by sequential hypergraphs to infer the dynamic user preferences with sequential user interactions.  ... 
arXiv:2202.04972v1 fatcat:5qwtw3sa4zfl3kjtvnjn6utshy
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