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  • Open Access


    Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

    R. Sujatha1,*, T. Abirami2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1775-1787, 2023, DOI:10.32604/csse.2023.031552

    Abstract The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in… More >

  • Open Access


    IoT-Deep Learning Based Activity Recommendation System

    Sharmilee Kannan1,*, R. U. Anitha2, M. Divayapushpalakshmi3, K. S. Kalaivani4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 2001-2016, 2023, DOI:10.32604/csse.2023.031965

    Abstract The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community. Health professionals are increasingly making use of the benefits of these technologies, resulting in a major improvement in health care both in and out of clinical settings. The Internet of Things (IoT) is a new internet revolution that is a rising research area, particularly in health care. Healthcare Monitoring Systems (HMS) have progressed rapidly as the usage of Wearable Sensors (WS) and smartphones have increased. The existing framework of conventional telemedicine’s store-and-forward method has some issues, including… More >

  • Open Access


    Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

    P. N. Ramesh1,*, S. Kannimuthu2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3845-3860, 2023, DOI:10.32604/iasc.2023.031329

    Abstract The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’ current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners’ skill level… More >

  • Open Access


    Deep Learning Enabled Social Media Recommendation Based on User Comments

    K. Saraswathi1,*, V. Mohanraj2, Y. Suresh2, J. Senthilkumar2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1691-1702, 2023, DOI:10.32604/csse.2023.027987

    Abstract Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect… More >

  • Open Access


    Hybrid Recommender System Using Systolic Tree for Pattern Mining

    S. Rajalakshmi1,*, K. R. Santha2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1251-1262, 2023, DOI:10.32604/csse.2023.024036

    Abstract A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.… More >

  • Open Access


    A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

    Son-Lam VU, Quang-Hung LE*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 471-483, 2023, DOI:10.32604/csse.2023.025897

    Abstract Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users’ demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a… More >

  • Open Access


    Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

    Adel Binbusayyis*

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 501-513, 2022, DOI:10.32604/iasc.2022.022239

    Abstract The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating… More >

  • Open Access


    An Auction-Based Recommender System for Over-The-Top Platform

    Hameed AlQaheri1,*, Anjan Bandyopadhay2, Debolina Nath2, Shreyanta Kar2, Arunangshu Banerjee2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5285-5304, 2022, DOI:10.32604/cmc.2022.021631

    Abstract In this era of digital domination, it is fit to say that individuals are more inclined towards viewership on online platforms due to the wide variety and the scope of individual preferences it provides. In the past few years, there has been a massive growth in the popularity of Over-The-Top platforms, with an increasing number of consumers adapting to them. The Covid-19 pandemic has also caused the proliferation of these services as people are restricted to their homes. Consumers are often in a dilemma about which subscription plan to choose, and this is where a recommendation system makes their task… More >

  • Open Access


    User Interaction Based Recommender System Using Machine Learning

    R. Sabitha1, S. Vaishnavi2,*, S. Karthik1, R. M. Bhavadharini3

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1037-1049, 2022, DOI:10.32604/iasc.2022.018985

    Abstract In the present scenario of electronic commerce (E-Commerce), the in-depth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User Interaction based Recommender System (UI-RS)… More >

  • Open Access


    A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations

    Abdelrahman H. Hussein, Qasem M. Kharma, Faris M. Taweel, Mosleh M. Abualhaj, Qusai Y. Shambour*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 661-675, 2022, DOI:10.32604/iasc.2022.020132

    Abstract Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and… More >

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