Novelty‐driven recommendation by using integrated matrix factorization and temporal‐aware clustering optimization

Raja, D. R. Kumar; Pushpa, S.

doi:10.1002/dac.3851

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…The model-based strategy seeks patterns in the datasets and utilizes data from that pattern to construct new models. It also employs some methods such as matrix factorization [46].…”

Section: Collaborative Filteringmentioning

confidence: 99%

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

et al. 2022

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A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.

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“…The model-based strategy seeks patterns in the datasets and utilizes data from that pattern to construct new models. It also employs some methods such as matrix factorization [46].…”

Section: Collaborative Filteringmentioning

confidence: 99%

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

et al. 2022

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“…The memory-based approach computes the resemblance among the appliers by computing the similarity function like cosine formula. The model-based approach uses some sophisticated methods like machine learning techniques to find patterns in the dataset and learn from them to employ the new data [124] and also some approaches like matrix factorization [125].…”

Section: Overview Of Collaborative Filtering Mechanismmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

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Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

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“…This paper (Raja and Pushpa 18 ) has proposed a movie recommendation approach using integrated matrix factorization and temporal-aware clustering optimization (NOMINATE). This approach uses temporal information to update the rules that is used to derive personalized preferences of the users through probabilistic matrix factorization.…”

Section: Related Workmentioning

confidence: 99%

Big data analytics for large‐scale UAV‐MBN in quantum networks using efficient hybrid GKM

Mahaveerakannan

C²

2019

Concurrency and Computation

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A primary important aspect of wireless network domain is known to be secured communication as the signals are emphatically available and broadcasted through the air. Quantum wireless networks are a hot topic of research in recent years that made researchers to avail new techniques in this field. Big data is one of the major aspects that support large-scale quantum network applications. As sensor nodes have resource constraints, it is more complex when using the traditional techniques of key establishment that only used for stable communication networks. Group-related applications are also one of the important aspects in wireless network for sending and receiving messages from multiple users. Unfortunately, maintenance of security for the group-oriented protocols is major issue due to the frequent changes in membership. In this paper, a hybrid GKM with re-keying procedure is proposed to obtain a secure communication for the remote system of unmanned autonomous vehicle (UAV) with mobile backbone network (MBN). Here, the group controller is selected based on the simplified hybrid energy efficient distributed (HEED) protocol and honey key encryption algorithm is used by the key management center (KMC) for generating and distribution of key for the group's controller. The secure communication is explored using the key exchange mechanism between the nodes. The joining and leaving of a node from the group are initiated by the re-keying process. The simulation results explain the improved performance of the proposed hybrid method when compared with other existing techniques in terms of privacy level, energy, memory, and time consumption. KEYWORDSgroup key management (GKM), hybrid energy efficient distributed (HEED), mobile backbone network (MBN), security, unmanned autonomous vehicle (UAV)

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Novelty‐driven recommendation by using integrated matrix factorization and temporal‐aware clustering optimization

Cited by 15 publications

References 30 publications

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

A Systematic Study on the Recommender Systems in the E-Commerce

Big data analytics for large‐scale UAV‐MBN in quantum networks using efficient hybrid GKM

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