Exploring New Vista of intelligent collaborative filtering: A restaurant recommendation paradigm

Roy, Arup; Banerjee, Soumya; Sarkar, Manash; Darwish, Ashraf; Elhoseny, Mohamed; Hassanien, Aboul Ella

doi:10.1016/j.jocs.2018.05.012

Cited by 30 publications

(7 citation statements)

References 43 publications

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“…Literature [18] suggests that considering that the similarity between users is not only related to the items that users overestimate but also to the degree of users' interest in the items, a similarity calculation method based on users' interest is proposed, which reduces the negative impact of data sparseness in traditional algorithms to some extent. Literature [19] calculates the mixed similar degree (SD) value between users according to the evaluation matrix of users and the news feature word matrix with time weight, which effectively improves the data sparseness problem in traditional algorithms. Literature [20,21] combines the CF algorithm based on projects with demographic statistics based on user groups, which effectively solves the cold start problem of users.…”

Section: Related Workmentioning

confidence: 99%

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Jin

Sun

2022

Wireless Communications and Mobile Computing

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With the vigorous development of new media technologies such as Internet of Things, big data, and cloud computing, data-based sensor news (SN) will become the trend of news reporting in the future and the new normal of news production. Under this background, this paper further analyzes the relationship between SN production mode and traditional news production, including the inheritance of traditional news production value concept, as well as the breakthrough and change in form, media, and effect. In this paper, collaborative filtering (CF) algorithm is improved to solve the problems of data sparseness, user interest migration, and scalability in CF technology. In the calculation of news content similar degree (SD), the influence of part of speech and position of feature words in news is also considered, and the time window is used to establish a model that adapts to the change of user interest with time. In this method, the contribution degree of different attributes to distinguishing users is considered, and the attribute SD between users is accurately calculated, which effectively improves the accuracy of SN data acquisition results.

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Section: Related Workmentioning

confidence: 99%

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Jin

Sun

2022

Wireless Communications and Mobile Computing

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“…The thesis deals with designing hybrid intelligent algorithms using soft computing techniques, bio-inspired algorithms, and probabilistic models. Specifically, the research introduces (a) Crowd-Sourcing based Group Recommendation Framework: a modified termite colony based hybrid movie recommendation framework is introduced to minimize the scalability problem, recommendation of high quality products, and minimization of the recommendation time [5] (b) Trusted Contextual Recommendation Framework: a fish school search algorithm based model is proposed to ensure the recommendation from reputed users, and a reduction of the recommendation hazards using artificial bee colony based simulated annealing algorithm [6] (c) Functional Retail Recommendation Framework: a termite colony based optimized model is introduced for product recommendation, predicted of stocks based on product consumption pattern, and prediction to increase the overall selling [7] (d) New Collaborative Filtering Framework: a rough-dragonfly hybrid is proposed to find the optimal neighbors of the active user, accurate rating prediction, and removal of data sparsity issue [8] (e) New Vista in Demographic Filtering Framework: a K-means-ant colony hybrid is introduced to recommend the best partners in matrimonial sites, intelligent noisy data removal mechanism prior to recommendation, and intelligent classification of the significant attributes [9].…”

Section: Methodsmentioning

confidence: 99%

Designing Hybrid Intelligence based Recommendation Algorithms: An Experience through Machine Learning Metaphor

Roy¹

2020

IJCAI

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This article presents a summarization of the doctoral thesis, which proposes efficient hybrid intelligent algorithms in recommendation systems. The development of effective recommendation algorithms for ensuring quality recommendation in a timely manner is a tricky task. Moreover, the traditional recommendation system is inadequate to cope up with the new technological trends. To overcome these issues, a batch of sophisticated recommendation systems has been discovered e.g. contextual recommendation, group recommendation, and social recommendation. The research work investigates and analyzes new genres of recommenders using nature-inspired algorithms, evolutionary algorithms, swarm intelligence algorithms, and machine learning techniques. The algorithms resolve some crucial problems of these recommenders. As a result, the more personalized recommendation is ensured. Povzetek: Povzetek doktorske disertacije, ki predlaga učinkovite hibridne inteligentne algoritme v priporočenih sistemih, raziskuje in analizira nove zvrsti priporočil z uporabo algoritmov po naravnih vzorih, evolucijskih algoritmov, algoritmov z roji in tehnik strojnega učenja.

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“…A session-based recurrent neural network recommendation model is proposed in literature [ 19 ]. A multirate depth learning model based on time recommendation is proposed in literature [ 20 , 21 ]. Literature [ 22 ] introduces the concept of similarity based on meta path, in which meta path is a path composed of a series of relationships defined between different object types.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Recommendation Model of Contemporary Pop Music Based on Knowledge Map

Zhang

2022

Computational Intelligence and Neuroscience

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With the advent of the era of big data, the rise of Web2.0 completely subverts the traditional Internet model and becomes the trend of today’s information age. Simultaneously, massive amounts of data and information have infiltrated various Internet companies, resulting in an increase in the problem of information overload. In the online world, learning how to quickly and accurately select the parts we are interested in from a variety of data has become a hot topic. Intelligent music recommendation has become a current research hotspot in music services as a viable solution to the problem of information overload in the digital music field. On the basis of precedents, this paper examines the characteristics of music in a comprehensive and detailed manner. A knowledge graph-based intelligent recommendation algorithm for contemporary popular music is proposed. User-defined tags are described as the free genes of music in this paper, making it easier to analyze user behavior and tap into user interests. It has been confirmed that this algorithm’s recommendation quality is relatively high, and it offers a new development path for improving the speed of searching for health information services.

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Exploring New Vista of intelligent collaborative filtering: A restaurant recommendation paradigm

Cited by 30 publications

References 43 publications

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Data Acquisition Method of Sensor News Based on Collaborative Filtering Algorithm

Designing Hybrid Intelligence based Recommendation Algorithms: An Experience through Machine Learning Metaphor

Intelligent Recommendation Model of Contemporary Pop Music Based on Knowledge Map

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