Deep learning with consumer preferences for recommender system

Gao, Tingwei; Li, Xiu; Chai, Yi; Tang, Youhua

doi:10.1109/icinfa.2016.7832066

Cited by 7 publications

(9 citation statements)

References 25 publications

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“…Li et al, 2018;Lin, 2014;Wakil et al, 2020 Badriyah et al, 2017;Baum & Spann, 2014;Beladev et al, 2016;Cha et al, 2019;Cheng et al, 2018;Chinchanachokchai et al, 2021;Choi et al, 2011;Christensen & Schiaffino, 2014;Christidis & Mentzas, 2013;Congying et al, 2016;W. Deng, 2020;Gao et al, 2017;Y. Huang et al, 2020;Jiao et al, 2016;Kim et al, 2022;Lee & Hosanagar, 2021;Lina et al, 2022;Ma et al, 2017;Virdi et al, 2020;Wakil et al, 2020;Yadav et al, 2018;Yin et al, 2018;Yunhui et al, 2022;Zhu et al, 2010) User Profile (Abdullah et al, 2010;Adomavicius et al, 2013;Alyari & Jafari Navimipour, 2018;Badriyah et al, 2017;Chadha & Kaur, 2015;Congying et al, 2016;W.…”

Section: Number Of Research Papers Selected Per Yearunclassified

Recommender systems: A systematic literature review, synthesis and framework for future capabilities

Bokadia,

Jain,

Singhi

2024

Multidiscip. Rev.

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This study offers a comprehensive exploration of recommender systems (RSs) with a focus on their influence on consumers’ purchase intentions in the realm of e-commerce. Employing the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) method, the authors identified and evaluated 908 high-quality papers to systematically categorize RS. This paper outlines these categories and reviews major developments within them, identifying significant constructs influencing consumer purchasing decisions. The outcome is a conceptual framework illustrating the interrelationships among these constructs, providing a novel contribution to the literature. This framework lays the groundwork for future studies in the field and provides valuable insights for marketing professionals seeking to develop RS-based strategies.

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Section: Number Of Research Papers Selected Per Yearunclassified

Recommender systems: A systematic literature review, synthesis and framework for future capabilities

Bokadia,

Jain,

Singhi

2024

Multidiscip. Rev.

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“…Recently, deep learning based techniques implementation RSs have proved helpful in alleviating these issues. It becomes easy to predict null rating values with deep learning [36]. Some deep learning-based RSs have also incorporated information from social networks to improve the recommendations.…”

Section: Deep Learning In Recommender Systemsmentioning

confidence: 99%

“…Collaborative deep learning has given more successful results when compared to traditional collaborative filtering techniques [37,38]. With deep learning, it is easy to predict the null rating values [36]. Deep collaborative filtering has also been tested for movie recommendation [37].…”

Section: Deep Learning In Recommender Systemsmentioning

confidence: 99%

“…Moreover, deep learning has also opened the doors for improving the accuracy of social recommender systems. [19], [37], [44], [46], [52] 2016 17 [16], [21], [24], [25], [28], [36], [40], [41], [45], [47], [57], [60], [61], [69], [70], [65], [71] 2017 20 [17], [18], [20], [22], [26], [27], [38], [42], [43], [51], [53], [62]- [64], [66], [68], [72]- [74], [78] VI. CONCLUSION A voluminous research has been done and is also proceeding in recommender systems using deep learning.…”

Section: Miscellaneousmentioning

confidence: 99%

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An Application-oriented Review of Deep Learning in Recommender Systems

Shokeen¹,

Rana²

2019

IJISA

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The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.

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“…The prediction algorithm is the essential module of every prediction system and directly impact the performance and prediction results of the system. Nowadays, deep neural network (DNN) has been used in various fields [2] of computer science, computer vision, and speech recognition,etc [8]- [10]. DNN is an emerging field of machine learning research [11], which is used to solve complex learning problems of multiple areas [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

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Deep learning with consumer preferences for recommender system

Cited by 7 publications

References 25 publications

Recommender systems: A systematic literature review, synthesis and framework for future capabilities

Recommender systems: A systematic literature review, synthesis and framework for future capabilities

An Application-oriented Review of Deep Learning in Recommender Systems

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

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