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A News Recommender System Considering Temporal Dynamics and Diversity [article]

Shaina Raza
2021 arXiv   pre-print
Our news recommender system can also work for unprofiled, anonymous and short-term readers, by leveraging the rich side information of the news items and by including the implicit feedback in our model  ...  Our system should be able to: (i) accommodate the dynamics in reader behavior; and (ii) consider both accuracy and diversity in the design of the recommendation model.  ...  RELATED WORK Temporal dynamics Temporal dynamics in recommender systems refer to the concept of accurately capturing user preferences over time.  ... 
arXiv:2103.12537v1 fatcat:7c4yojhp35asdhugtobwgwfcau

TSCMF: Temporal and social collective matrix factorization model for recommender systems

Hamidreza Tahmasbi, Mehrdad Jalali, Hassan Shakeri
2020 Journal of Intelligent Information Systems  
In real-world recommender systems, user preferences are dynamic and typically change over time.  ...  Moreover, based on the intuition that social influence can affect the users' preferences in a recommender system, we propose a Temporal and Social Collective Matrix Factorization model called TSCMF for  ...  In this regard, a series of studies based on MF exploit the side information in temporal recommendation systems.  ... 
doi:10.1007/s10844-020-00613-w fatcat:ps7cwdeqnbdjhcird2ek4q4w6e

An integrated personalization framework for SaaS-based cloud services

Haolong Fan, Farookh Khadeer Hussain, Muhammad Younas, Omar Khadeer Hussain
2015 Future generations computer systems  
The approach we adapt in the design and development of the proposed framework is to synthesize various models and techniques in a novel way.  ...  Software as a Service (SaaS) has recently emerged as one the most popular service delivery models in cloud computing.  ...  CPRS [15] is a cloud-based recommendation system for TV platforms. In this system, the client-side is used to record user habits and receive programs.  ... 
doi:10.1016/j.future.2015.05.011 fatcat:iqic2upcmfevrpejim732ydzdm

A Deep Temporal Neural Music Recommendation Model Utilizing Music and User Metadata

Hai-Tao Zheng, Jin-Yuan Chen, Nan Liang, Arun Sangaiah, Yong Jiang, Cong-Zhi Zhao
2019 Applied Sciences  
To address these issues, we proposed a Deep Temporal Neural Music Recommendation model (DTNMR) based on music characteristics and the users' temporal preferences.  ...  DTNMR alleviates the cold start problem in the item side using the music medadata and discovers new users' preferences immediately after they listen to music.  ...  In DTNMR, we combine linear features and embedded features, users' dynamic preferences and intrinsic preferences, temporal information and non-temporal information together to recommend songs for users  ... 
doi:10.3390/app9040703 fatcat:qptc25uqmvhd5jme3yqgv7tgjy

Graph Neural Networks in Recommender Systems: A Survey [article]

Shiwen Wu, Fei Sun, Wentao Zhang, Bin Cui
2021 arXiv   pre-print
Owing to the superiority of GNN in learning on graph data and its efficacy in capturing collaborative signals and sequential patterns, utilizing GNN techniques in recommender systems has gain increasing  ...  In this survey, we provide a comprehensive review of the most recent works on GNN-based recommender systems. We proposed a classification scheme for organizing existing works.  ...  Why Graph Neural Network for Recommendation In Recommender systems aim to recommend items for users based on user-item interactions and side information if available.  ... 
arXiv:2011.02260v2 fatcat:5phmgwzndfg7xlquierooryp2a

A generalized model via random walks for information filtering

Zhuo-Ming Ren, Yixiu Kong, Ming-Sheng Shang, Yi-Cheng Zhang
2016 Physics Letters A  
Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks.  ...  Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree  ...  Acknowledgements We acknowledge Rui Xiao for fruitful discussion and assistance.  ... 
doi:10.1016/j.physleta.2016.06.009 fatcat:lamipdwqrzcz3nuaqioup2yawm

Dynamic Intention-Aware Recommendation System [article]

Shuai Zhang, Lina Yao
2017 arXiv   pre-print
In this paper, we propose a dynamic intention-aware recommender system to better facilitate users to find desirable products and services.  ...  Recommender systems have been actively and extensively studied over past decades. In the meanwhile, the boom of Big Data is driving fundamental changes in the development of recommender systems.  ...  Dynamics in Recommendation System Dynamics in recommendation system mainly consist of temporal status and spatial information.  ... 
arXiv:1703.03112v2 fatcat:af3jq4emcna6heoyujwxwqdmgu

A Novel Recommendation Algorithm Incorporating Temporal Dynamics, Reviews and Item Correlation

Ting WU, Yong FENG, JiaXing SANG, BaoHua QIANG, YaNan WANG
2018 IEICE transactions on information and systems  
Recommender systems (RS) exploit user ratings on items and side information to make personalized recommendations.  ...  In order to recommend the right products to users, RS must accurately model the implicit preferences of each user and the properties of each product.  ...  Conclusion and Future Work In the study of recommender systems, besides the explicit ratings, side information like temporal dynamics, reviews information and item correlation provide both opportunities  ... 
doi:10.1587/transinf.2017edp7387 fatcat:eyefclz6qre2xfvycsjuqrftsu

Intelligent interaction based on holographic personalized portal

Yadong Huang, Yueting Chai, Yi Liu, Xiang Gu
2017 International Journal of Crowd Science  
Purpose -The purpose of this paper is to study the architecture of holographic personalized portal, user modeling, commodity modeling and intelligent interaction.  ...  Design/methodology/approach -In this paper, the authors propose crowd-science industrial ecological system based on holographic personalized portal and its interaction.  ...  Psychology in recommendation The traditional recommender systems only consider the material demand in the analysis of user preferences and ignore the psychological factors.  ... 
doi:10.1108/ijcs-08-2017-0016 fatcat:thdq5xvzfnaydflgpivqm5gvju

A Survey on Knowledge Graph-Based Recommender Systems [article]

Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He
2020 arXiv   pre-print
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences.  ...  In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest.  ...  Currently, most KG-based recommender systems build the graph by incorporating item side information, while few models consider user side information.  ... 
arXiv:2003.00911v1 fatcat:qhyca7pu3beqtk6x55kpggowea

Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
2019 arXiv   pre-print
Recommender systems have become an essential tool to help resolve the information overload problem in recent decades.  ...  This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.  ...  Central Universities in China under Grant No.  ... 
arXiv:1909.12807v2 fatcat:2nj4crzcd5attidhd3kneszmki

Interacting Attention-gated Recurrent Networks for Recommendation [article]

Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M.J. Tax
2017 arXiv   pre-print
Capturing the temporal dynamics of user preferences over items is important for recommendation.  ...  In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping  ...  This work is partially supported by the SIMTech-NTU Joint Lab on Complex Systems.  ... 
arXiv:1709.01532v2 fatcat:wawwv6jyrzecjhrd7uljb7slbq

FSCR: A Deep Social Recommendation Model for Misleading Information

Depeng Zhang, Hongchen Wu, Feng Yang
2021 Information  
show users' real interests and will not be easily affected by misleading information), and then we create a deep social recommendation model which fuses user side information called FSCR.  ...  In addition, to deal with the misleading information, we divide user information into two types, namely explicit preference information (explicit comments or ratings) and user side information (which can  ...  We introduce the FSCR model, which fuses user side information, historical preference features, and social trust features to build the user model and make recommendations. 3.  ... 
doi:10.3390/info12010037 fatcat:4lqdqaladzbgfgcj3lcaxxewxm

Relation Embedding for Personalised POI Recommendation [article]

Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz
2020 arXiv   pre-print
side-information.  ...  However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations.  ...  Acknowledgments We acknowledge the support of Australian Research Council Discovery DP190101485, Alexander von Humboldt Foundation, and CSIRO Data61 Scholarship program.  ... 
arXiv:2002.03461v2 fatcat:uscrnbl6qrdela3moehxtes7tq

Relation Embedding for Personalised Translation-Based POI Recommendation [chapter]

Xianjing Wang, Flora D. Salim, Yongli Ren, Piotr Koniusz
2020 Lecture Notes in Computer Science  
Location-based POI recommendation systems utilize the temporal context [24] for the purpose of modeling personal preferences.  ...  side-information.  ...  We acknowledge the support of Australian Research Council Discovery DP190101485, Alexander von Humboldt Foundation, and CSIRO Data61 Scholarship program.  ... 
doi:10.1007/978-3-030-47426-3_5 fatcat:zron5negfbea5lqfgnmz6rimuq
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