Papers by Bam Bahadur Sinha
Improved equilibrium optimization based on Levy flight approach for feature selection
Evolving Systems
The long tail of diverse consumption of resources online by the customers raises a challenge for ... more The long tail of diverse consumption of resources online by the customers raises a challenge for the e-commerce websites and service providers. Recommender system offers a vigorous way to cope up with the aforementioned challenge. In this paper, we have proposed a hybrid cohort rating prediction technique which relies on high cohort users and high cohort items to make predictions. Our model significantly improves the retention of recommender system showing encouraging results when compared with existing traditional recommender systems.
Scalable resource description framework clustering: A distributed approach for analyzing knowledge graphs using minHash locality sensitive hashing
Concurrency and Computation: Practice and Experience, 2022
Diversification-oriented accuracy prediction in Recommender Systems
International Journal of Industrial and Systems Engineering
DNN-MF: deep neural network matrix factorization approach for filtering information in multi-criteria recommender systems
Neural Computing and Applications
Building an Adaptive Recommendation Model Based on Fuzzy MP Neuron and Weighted Similarity Indicator
J. Inf. Sci. Eng., 2021
An effective movie recommender system enhanced with time series analysis of user rating behaviour
International Journal of Mathematics in Operational Research, 2021
Pattern Analysis and Applications, 2020
This paper proposes a novel behavior-inspired recommendation algorithm named TimeFly algorithm, w... more This paper proposes a novel behavior-inspired recommendation algorithm named TimeFly algorithm, which works on the idea of altering behavior of the user with respect to time. The proposed model considers solving two recommendation problems (fluctuating user interest over time and high computation time when dataset shifts from scarcity to abundance) and presents a real application of the proposed method in the field of recommendation engine. It describes a system which enrolls the changing behavior of user to furnish personalization suggestions. The results obtained by TimeFly are compared with the results of other well-known algorithms. Simulation results on 100K, 1M, 10M, and 20M MovieLens dataset reveal that using TimeFly leads to high accurate predictions in less computation time.
Building a fuzzy logic-based McCulloch-Pitts Neuron recommendation model to uplift accuracy
The Journal of Supercomputing, 2020
Recommender system is one of the most popular technique used for information filtering. It helps ... more Recommender system is one of the most popular technique used for information filtering. It helps in discovering hidden knowledge patterns from a large set of ubiquitous products and services. The most popular approaches such as collaborative filtering suffers from the complication of data sparsity, overspecification and high computation complexity when dataset drifts from scarcity to abundance. In this regard, we developed a hybrid model that contemplates between accuracy and computation time in order to generate a real-time most relevant items for the users. We made use of imputation technique, fuzzy logic using novel similarity technique and McCulloch-Pitts(MP) Neuron to cope up with aforementioned complications. The experimental evaluation on MovieLens dataset shows that the proposed model yields high efficiency and effectiveness. We tested the resultant classification accuracy of our proposed model using precision, recall and f1-score.
Building a Fuzzy Logic-Based Artificial Neural Network to Uplift Recommendation Accuracy
The Computer Journal, 2019
With the advent of the internet, the recommender system escorts the users in a customized way to ... more With the advent of the internet, the recommender system escorts the users in a customized way to nominate items from a massive set of possible alternatives. The emergence of overspecification in recommender system has emphasized negative effects on the context of prediction. The drift of user interest over time is one of the challenging affairs in present personalized recommender system. In this paper, we present a neural network model to improve the recommendation performance along with usage of fuzzy-based clustering to decide membership value of users and matching imputation to cutback sparsity to some extent. We evaluate our model on the MovieLens dataset and show that our model not only elevates accuracy, but also considers the order in which recommendation should be given. We compare the proposed model with a number of state-of-the-art personalization methods and show the dominance of our model using accuracy metrics such as root-mean-square error and mean absolute error.
Journal of King Saud University - Computer and Information Sciences, 2019
In the past few decades recommender system has reshaped the way of information filtering between ... more In the past few decades recommender system has reshaped the way of information filtering between websites and the users. It helps in identifying user interest and generates product suggestions for the active users. This paper presents an enlightening analysis of various recommender system such as content-based, collaborative-based and hybrid recommendation techniques along with few optimization models that has been applied to improvise the parameters being considered by the aforementioned techniques. We explored 125 articles published from 1992 to 2019 in order to discuss the problems associated with the existing models. Various advantages and disadvantages of each recommendation model including the input methods has been elaborated. Critical review on research problems based on the explored techniques and future directions has also been covered.
Soft Computing, 2019
Recommender system plays a supporting role in the process of information filtering. It plays a re... more Recommender system plays a supporting role in the process of information filtering. It plays a remarkable role in largescale online shopping and product suggestions. This paper discusses various trends of recommender system such as content-based, collaborative-based and hybrid personalization techniques proposed for recommendations. It provides better insight and future directions of recommender systems. We have reviewed 142 articles from several journals and conference papers which were published from 1992 to 2019. We have used statistical descriptions to show the progression and drawbacks of the various notions of recommendation approaches. We have also discussed growing research demand in the area of recommender systems as well as the pros and cons of the currently available classifications. We have created a classification of recommender techniques, including various user inputs, knowledge from the database, the ways in which the recommendation will be presented to the user and the technologies which are used to create the recommendations. Keywords Content filtering Á Collaboration Á Knowledge filtering Á Community filtering Á Hybrid filtering Á Similarity measures Communicated by A. Di Nola.
Diversifying the Predictions in the Recommender Systems
International Journal of Business Information Systems, 2020
2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), 2018
Статья посвящена исследованию возможности отнесения к принципам конституционного статуса личности... more Статья посвящена исследованию возможности отнесения к принципам конституционного статуса личности такой интегральной категории как автономия личности. Автор раскрывает обоснованность такого подхода и его необходимость в условиях современной социальной действительности. В статье рассматривается соотношение принципов свободы и автономии личности в их взаимосвязи и различии. Отмечается взаимодействие принципа автономии личности со всеми правами и свободами личности, его соответствие основным признакам конституционных принципов. Также указывается на значение принципа автономии личности как источника развития конституционных правоотношений, одновременно определяющего его направление в сторону обеспечения безопасности и свободной реализации личности в условиях возрастающего принудительного информационного воздействия социального пространства.Автономии личности автором отводится роль основного концепта конституционного статуса личности, отражающего необходимость и возможность свободного и творческого самоопределения личности, осуществление личностью самостоятельного выбора мышления и поведения, становление и реализация самобытности личности.
Recent advancements and challenges of Internet of Things in smart agriculture: A survey
Future Generation Computer Systems, 2022
A recommender system based on a new similarity metric and upgraded crow search algorithm
Journal of Intelligent & Fuzzy Systems, 2020
In the current era of big data, the recommender system aspires to provide users with a tailored s... more In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the Crow Search Algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is le...
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Papers by Bam Bahadur Sinha