An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle

Tan, Zhenhua; He, Liangliang

doi:10.1109/access.2017.2778424

Cited by 78 publications

(49 citation statements)

References 26 publications

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“…In addition to these approaches, authors also proposed a hybrid approach in recommendation systems to analyze the customer behavior by using rating data of the customer review and textual content as well. Tan et al [12] has worked on a novel similarity measure inspired by a physical resonance phenomenon, named resonance similarity (RES) is proposed proving superior predictive accuracy as compared to the existing similarity measures on users' evaluations. Crespo et al [45] discuss that Sem-Fit uses the customers' experience point of view in order to apply fuzzy logic methods to relating customer and hotel characteristics, represented by means of domain ontologies and affect grids.…”

Section: Recommender Systemsmentioning

confidence: 99%

“…× F − m = 2 P recision+Recall P recision×Recall (13) Above two metrics represented by equation (11) and (12) are used to calculate F-measure represented by equation (13), F-measure is a weighted combination of the Precision and recall metrics. The traditional F-measure is the Harmonic Mean of Precision and Recall and here it is used in this paper to evaluate the accuracy and efficiency of the recommendations in the proposed Recommender application.…”

Section: Easurementioning

confidence: 99%

“…According to Zhang et al [9] Collaborative Filtering (CF) based approach is very successful technology in all RSs. The [10], [11] has reported that there are three fundamental challenges faced by CF approaches such as − Cold start challenge rise where an item appears which has not been rated before, recommendations cannot be made for it or when a new user without any prerecorded profile appears [12,13].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Recommender Systemsmentioning

confidence: 99%

Section: Easurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Ramzan

Bajwa

Jamil

et al. 2019

Scientific Programming

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“…where d max denotes the maximum allowable propagation distance among users and d uv denotes the trust statement between users u and v. In this section, the dimensions of data first are reduced as (16) and (17) before calculating the similarity. To ensure the accuracy of the predictions, a reliability measure in [7,24,55] is employed to evaluate the quality by providing a feedback on the quality of the predicted rates. The reliability measure is calculated as follows [7,24] (see (24)):…”

Section: Definition 2 Average Similarity Standard Deviation (Assd)mentioning

confidence: 99%

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

Chen

Hua

Gao

et al. 2018

Mathematical Problems in Engineering

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Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in accuracy.

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“…Section 4 will bring RMSE (root mean squared error) results of our exponential similarity measures compared to Manhattan, Euclidean, Jaccard, Cosine and Pearson similarity measures by using the algorithm mentioned. [2][3][4]. Model-based collaborative filtering methods develop predictions based off of models that are created by data mining and machine learning techniques, whereas memory-based collaborative filtering approaches handle predictions by finding most similar users (or items) to the current user (or item) that we want to predict ratings for, and does this by means of using those similar user ratings [5].…”

Section: Introductionmentioning

confidence: 99%

An exponential similarity measure for collaborative filtering

et al. 2019

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In this paper, we propose two exponential similarity measures for collaborative filtering in recommender systems. The proposed similarity measures are used to estimate the distance between two users or items. Furthermore, an algorithm is proposed to use the distance obtained from similarity measures introduced in this paper to evaluate empty rating elements in the rating matrix and compare the results of these similarity measures with our exponential similarity measures. Therefore, we deduce that the algorithm using the exponential similarity measures works better than the algorithm using Manhattan, Euclidean, Jaccard, Cosine or Pearson similarity measures when taking K-Most-Similar-Users to evaluate the similarity in order to find ratings for the user that we want to predict ratings for.

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An Efficient Similarity Measure for User-Based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle

Cited by 78 publications

References 26 publications

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

An exponential similarity measure for collaborative filtering

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