An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems

Shambour, Qusai; Hourani, Mou'ath; Fraihat, Salam

doi:10.14569/ijacsa.2016.070837

Cited by 52 publications

(46 citation statements)

References 25 publications

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“…Several researchers have applied collaborative filtering algorithms [31][32][33][34][35][36][37] for recommendations. Some of them are discussed below.…”

Section: Recommender Systemsmentioning

confidence: 99%

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Ramzan

Bajwa

Jamil

et al. 2019

Scientific Programming

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This paper presents an intelligent approach to handle heterogeneous and large-sized data using machine learning to generate true recommendations for the future customers. The Collaborative Filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type as additional information for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommend hotel class based on guest type using fuzzy rules. Different experiments are performed over the real world dataset obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated and results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.

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“…Several researchers have applied collaborative filtering algorithms [31][32][33][34][35][36][37] for recommendations. Some of them are discussed below.…”

Section: Recommender Systemsmentioning

confidence: 99%

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Ramzan

Bajwa

Jamil

et al. 2019

Scientific Programming

View full text Add to dashboard Cite

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“…The experiments were conducted using a multi-criteria dataset of real-users from a tourism domain (www.tripadvisor.com). In Shambour et al (2016), an item-based multi-criteria RS was proposed for enhancing personalised recommendations of items. The approach integrates items' semantic information and the criteria ratings of the items for addressing some of the outstanding limitations of the traditional item-based CF technique.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the performance of a neural network-based multi-criteria recommender system

Hassan

Hamada

2019

IJSTDS

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Frequent use of internet applications and rapid increase in volumes of resources have made it difficult for online users to effectively make decisions on the kinds of information or items to select. Recommender systems (RSs) are intelligent decision-support tools that exploit preferences of users and suggest items that might be interesting to them. RSs are one of the various solutions proposed to address the problems of information overload. Traditionally, RSs use single rating techniques to predict and represent preferences of users for items that are not yet seen. Multi-criteria RSs use multiple ratings to various attributes of items for improving prediction and recommendation accuracy of the systems. However, one major challenge of multi-criteria RSs is the choice of an efficient approach for modelling the criteria ratings. Therefore, this paper aimed at employing artificial neural networks to model the criteria ratings and determine the predictive performance of the systems based on aggregation function approach. Seven evaluation metrics have been used to evaluate and measure the accuracy of the systems. The empirical results of the study have shown that the proposed technique has the highest prediction and recommendation than the corresponding traditional technique.Reference to this paper should be made as follows: Hassan, M. and Hamada, M. (2019) 'Evaluating the performance of a neural network-based multi-criteria recommender system', Int.Evaluating the performance of a neural network-based multi-criteria 55 Mohamed Hamada is currently a Senior Associate Professor working in the Software Engineering Lab, at the University of Aizu, Aizuwakamatsu, Fukushima, Japan. He obtained his PhD from the University of Tsukuba, Japan. His research interest is in artificial intelligence and the study of functional logic languages, natural inspired computation, cloud computing-based e-learning and m-learning systems, multimedia, and web-based and advanced learning technologies. He is also interested in smart devices (such as smartphones and tablets) applications development and innovation, and so on. He is a regular Visiting Professor at Fatih University, Istanbul, Turkey and the African This paper is a revised and expanded version of a paper entitled 'Performance analysis of neural networks-based multi-criteria recommender systems' presented at

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“…This related work contemplates: (1) multi-criteria tourism crowdsourced ratings in hotel recommendation systems; (2) collaborative filtering; and (3) trust-based modelling. Adomavicius and Kwon [2], Bilge and Kaleli [4], Lee and Teng [24], Jhalani et al [17], Liu et al [26], Manouselis and Costopoulou [27] and Shambour et al [32] have explored the integration of multi-criteria ratings in the user profile, mainly using multimedia datasets to validate their proposals. Davoudi et al [7], Jia et al [18] and Zhang et al [37] have explored the trust modelling for rating prediction presenting trust models together with matrix factorisation algorithms or similarity metrics.…”

Section: Related Workmentioning

confidence: 99%

Trust-based Modelling of Multi-criteria Crowdsourced Data

et al. 2017

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As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.

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An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems

Cited by 52 publications

References 25 publications

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Evaluating the performance of a neural network-based multi-criteria recommender system

Trust-based Modelling of Multi-criteria Crowdsourced Data

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