Traditional Similarity Measures Research Articles

New custom rating for improving recommendation system performance

Recommendation system is currently attracting the interest of many explorers. Various new businesses have surfaced with the rise of online marketing (E-Commerce) in response to Covid-19 pandemic. This phenomenon allows recommendation items through a system called Collaborative Filtering (CF), aiming to improve shopping experience of users. Typically, the effectiveness of CF relies on the precise identification of similar profile users by similarity algorithms. Traditional similarity measures are based on the user-item rating matrix. Approximately, four custom ratings (CR) were used along with a new rating formula, termed New Custom Rating (NCR), derived from the popularity of users and items in addition to the original rating. Specifically, NCR optimized recommendation system performance by using the popularity of users and items to determine new ratings value, rather than solely relying on the original rating. Additionally, the formulas improved the representativeness of the new rating values and the accuracy of similarity algorithm calculations. Consequently, the increased accuracy of recommendation system was achieved. The implementation of NCR across four CR algorithms and recommendation system using five public datasets was examined. Consequently, the experimental results showed that NCR significantly increased recommendation system accuracy, as evidenced by reductions in RMSE, MSE, and MAE as well as increasing FCP and Hit Rate. Moreover, by combining the popularity of users and items into rating calculations, NCR improved the accuracy of various recommendation system algorithms reducing RMSE, MSE, and MAE up to 62.10%, 53.62%, 65.97%, respectively, while also increasing FCP and Hit Rate up to 11.89% and 31.42%, respectively.

Time-Aware Based Recommendation System using Gower’s Coefficients: Enhancing Personalized Recommendation

To give users personalised recommendations, the collaborative filtering technique is widely used. In this technique, similarity computation plays a crucial role. Once the similarities among users or items are determined, the system can predict how a user might rate or interact with items that they have not yet encountered. The traditional approaches or measures mainly considered the user-item historical ratings to compute the similarity, while user preferences may change with time. Considering this, the objective of this research is to create an efficient recommendation system that utilized the temporal data. For this, few time decay functions, i.e., exponential, linear, logistic, and power applied to the ratings to give more weightage to the most recent ratings. Since, Gower’s coefficient is suitable for handling the missing data, it is applied to calculate the similarity and compared its results with popular traditional similarity measures i.e., Cosine and Pearson correlation coefficient. Experimental findings on the ML-100k dataset in terms of Root Mean Squared Error (RMSE) and Mean Average Error (MAE) demonstrate that performance of RS improved when we applied a power function on ratings. With comparison to most recent methods PIP, NHSM, RJaccard etc., proposed approach gives almost 10% better results in MAE and 5% better results in RMSE comparison.

A similarity measure of complex-valued evidence theory for multi-source information fusion

The study of complex-valued evidence theory has become an interesting topic, particularly in the context of information fusion techniques. These studies mainly focus on its geometric interpretation and application. Moreover, little work has been conducted with regard to conflict measure. Therefore, this paper defines a complex similarity measure which quantifies the degree of conflict among complex-valued mass functions by measuring the degree of consistency among complex-valued mass functions. It also satisfies some basic properties, such as non-negativity, symmetry and etc. Complex similarity measure is an extension of traditional similarity measure in complex space, which will collapse to a similarity measure of real space when complex-valued mass functions collapse to mass functions of real space. Furthermore, some numerical examples illustrate the rationality and effectiveness of complex similarity measure. By employing complex similarity measure, this paper proposes a method to combine information and reduce the time cost associated with complex-valued Dempster's rule of combination. Experimental results on datasets indicate that for some data sets (Take iris for example), the proposed method can not only reduce the time cost (75%), but also improve the accuracy of fusion results (96%).

Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

ABSTRACT Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user’s interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users’ interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set.

An Enhanced Quantum K-Nearest Neighbor Classification Algorithm Based on Polar Distance

The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data. The quantum K-nearest neighbor (QKNN) algorithm can handle the above problem with satisfactory efficiency; however, its accuracy is sacrificed when directly applying the traditional similarity measure based on Euclidean distance. Inspired by the Polar coordinate system and the quantum property, this work proposes a new similarity measure to replace the Euclidean distance, which is defined as Polar distance. Polar distance considers both angular and module length information, introducing a weight parameter adjusted to the specific application data. To validate the efficiency of Polar distance, we conducted various experiments using several typical datasets. For the conventional KNN algorithm, the accuracy performance is comparable when using Polar distance for similarity measurement, while for the QKNN algorithm, it significantly outperforms the Euclidean distance in terms of classification accuracy. Furthermore, the Polar distance shows scalability and robustness superior to the Euclidean distance, providing an opportunity for the large-scale application of QKNN in practice.

Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique

The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.

The measurements of the similarity of dynamic brain functional network

Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.

Knowledge Service Technology for Supporting Intelligent Product Design

In this paper, a knowledge service method that supports the intelligent design of products is investigated. The proposed method provides the solutions to computational problems and reasoning and decision-making problems in the field of intelligent design. The requirement analysis of a knowledge-based intelligent design system integrates design knowledge into case-based reasoning activities through scheme analysis, scheme evaluation, and scheme adjustment, thus achieving knowledge-based intelligent reasoning and decision-making. During the similarity matching, a new hybrid similarity measurement method is proposed to calculate the similarity of crisp and fuzzy sets. This method integrates the fuzzy set similarity theory based on the traditional similarity measurement method. A method of attribute level classification is proposed to assign weight coefficients. The attributes are divided into the primary matching and auxiliary matching levels according to the decisiveness of case matching, and the set of weight coefficients is continuously and dynamically updated through case-based reasoning learning. Then, the weighted global similarity measure is used to obtain the set of similar cases from the case database. Finally, a design example of a computer numerical control tool holder product is studied to present the practicability and effectiveness of the proposed method.

Research on Similarity Measurement Algorithm of High-dimensional Data

The similarity measurement of high-dimensional data is an important research content in data mining and other fields. Aiming at the shortcomings of the traditional similarity measurement algorithms, a new measurement algorithm is proposed. The results of comparison with traditional algorithms show that new algorithm can quantitatively and intuitively reflect the similarity of two sets of high-dimensional data, and has strong adaptability and superiority.

An optimized item-based collaborative filtering algorithm

Collaborative filtering over the years have emerged as an alternative recommender system to address some of the setbacks of content based filtering. Although, Collaborative filtering has offered some benefits to the majority of the online stores in recommending products to users using users’ ratings of similarity measure, its usage has also raised some doubt in the minds of researchers, regarding its effectiveness in handling ratings with limited number of users or no rating record from users. Thus, this has resulted in efforts by researchers in determining further ways to combat the issues attributed with the existing collaborative filtering techniques in terms of data sparsity or cold-start situations. This study focused on improving the traditional similarity measurements that currently exist on the item-based collaborative filtering, in order to accommodate and mitigate further the issue of cold-start situations. Thus, this study proposed an algorithm which is meant to balance the three current traditional measurement metrics such as: Cosine-based similarity, Pearson correlation similarity and Adjusted cosine similarity, in the direction of cold-start situations. The improved algorithm of traditional measurement metrics were further compared with the existing algorithm of the traditional metrics. Results showed that the proposed algorithm offered a better item-based collaborative filtering algorithm to recommendation systems than the existing, using data set from Movielens recommender system. Hence, the proposed algorithm did not only mitigate the drawbacks experienced with the three traditional algorithms in terms of data sparsity or cold-start situations but also retained the good features of the existing item-based collaborative filtering algorithm. Thus, the proposed algorithm complemented the strength of the three traditional measurement metrics with evidence shown when measured with Mean Absolute Error.

The colour block registration method of fuzzy image under multi-layer P-spline geometric transformation

In order to overcome the problem that the traditional similarity measure is easy to be affected by the grey-scale migration, this paper proposes a colour block registration method of fuzzy image under the multi-layer P-spline geometric transformation. This method mainly sets sparse coding as similarity measure, and divides two images that are not registered into image blocks. K-singular value decomposition (K-SVD) algorithm is used to train image blocks, acquire analysis dictionary and find sparse coefficients. The multi-layer P-spline geometric transformation is used to simulate the fuzzy image, and the gradient descent method is used to optimise the objective function to complete the colour block registration of the fuzzy image under the multi-layer P-spline geometric transformation. The experimental results show that the registration time is the lowest, the root mean square error is kept below 0.04, and the registration accuracy is up to 100%.

The colour block registration method of fuzzy image under multi-layer P-spline geometric transformation

In order to overcome the problem that the traditional similarity measure is easy to be affected by the grey-scale migration, this paper proposes a colour block registration method of fuzzy image under the multi-layer P-spline geometric transformation. This method mainly sets sparse coding as similarity measure, and divides two images that are not registered into image blocks. K-singular value decomposition (K-SVD) algorithm is used to train image blocks, acquire analysis dictionary and find sparse coefficients. The multi-layer P-spline geometric transformation is used to simulate the fuzzy image, and the gradient descent method is used to optimise the objective function to complete the colour block registration of the fuzzy image under the multi-layer P-spline geometric transformation. The experimental results show that the registration time is the lowest, the root mean square error is kept below 0.04, and the registration accuracy is up to 100%.

Feature similarity measurement of cross-age face images based on a deep learning algorithm

In order to overcome the problem of low accuracy of traditional facial image feature similarity measurement methods, the paper proposes a new cross-age facial image feature similarity measurement method based on a deep learning algorithm. Face segmentation is performed based on eye coordinates to determine the effective area for face detection. This study introduces deep learning algorithms to build the basic architecture of deep learning networks, define network input as face image data containing age and identity tags in a cross-age database, and define output as face image features under constant age, and pass the foundation separately training and cross-age training complete the network training, and calculate the facial features under the same age. The cosine distance method is used to measure the similarity of face image features across ages. The experimental results show that the method has obtained more accurate measurement results in different age data sets, and the highest measurement accuracy is 97.9%.

Research on Collaborative Filtering of Food Information Security in E-Commerce Platform

Recommender systems are one of the most important technologies in the electronic commerce system. In a collaborative filtering recommendation algorithm, similarity calculation is the key to determining the efficiency of the recommendation algorithm. This paper analyzes the shortcomings of traditional similarity measurement methods in recommender systems and proposes a scoring-matrix-filling algorithm. Based on information categories and user interest similarity, the algorithm can reduce the negative influence of data sparsity on the recommendation result to some extent. The research results have certain practical and guiding significance.

Unknown defect detection for printed circuit board based on multi‐scale deep similarity measure method

Defect detection with high precision is of great significance for printed circuit board (PCB) fabrication. Due to the lack of priori knowledge of categories and shape features, detection of unknown defects faces greater challenges than that of common defects. Inspired by similarity measurement, this study proposes a multi-layer deep feature fusion method to calculate the similarity between template and defective circuit board. Compared with conventional methods which divide the whole detection into two independent parts of hand-designed features and similarity measurement, the authors end-to-end model is designed to combine these two parts for joint optimisation. First, the Siamese network is utilised as their backbone architecture for feature extraction of pairwise images. And then the spatial pyramid pooling network is incorporated into the feature maps of each convolutional module to fuse the multi-scale feature vectors. Finally, the discriminative feature embedding and similarity metric are obtained by using the contrastive loss during the training process. Experimental results show that the proposed model has better performance in detecting and locating unknown defects in bare PCB images than traditional similarity measurement methods. Moreover, our method is promising for further improvement of defect detection with less training image pairs and more accurate detection results.

Protein2vec: Predicting Protein-Protein Interactions Based on LSTM.

The semantic similarity of gene ontology (GO) terms is widely used to predict protein-protein interactions (PPIs). The traditional semantic similarity measures are based mainly on manually crafted features, which may ignore some important hidden information of the gene ontology. Moreover, those methods usually obtain the similarity between proteins from similarity between GO terms by some simple statistical rules, such as MAX and BMA (best-match average), oversimplifying the possible complex relationship between the proteins and the GO terms annotated with them. To overcome the two deficiencies, we propose a new method named protein2vec, which characterizes a protein with a vector based on the GO terms annotated to it and combines the information of both the GO and known PPIs. We firstly try to apply the network embedding algorithm on the GO network to generate feature vectors for each GO term. Then, Long Short-Time Memory (LSTM) encodes the feature vectors of the GO terms annotated with a protein into another vector (called protein vector). Finally, two protein vectors are forwarded into a feedforward neural network to predict the interaction between the two corresponding proteins. The experimental results show that protein2vec outperforms almost all commonly used traditional semantic similarity methods.

A Deformable 3D-3D Registration Framework Using Discrete Periodic Spline Wavelet and Edge Position Difference

Neck pain is one of the most common symptoms of cervical spine disease, and segmenting neck muscles to create volumetric measurements may assist clinical diagnosis. While image registration is used to segment medical images, registration is highly challenging for neck muscles due to their tight proximity, shape and size variations among subjects, and similar appearance. These challenges cause conventional multi resolution-based registration methods to be trapped in local minima due to their low degree of freedom geometrical transforms. This article presents a novel object-constrained hierarchical registration framework for aligning inter-subject neck muscles. First, to handle large scale local minima, the proposed framework uses a coarse registration technique, which optimizes the new edge position difference (EPD) similarity measure, to align large mismatches. Also, a new transformation based on the discrete periodic spline wavelet (DPSW), affine and free-form-deformation (FFD) transformations are exploited. Second, to avoid monotonous nature of using transformations in multiple stages, a fine registration technique is designed for aligning small mismatches. This technique uses a double-pushing system by changing edges in the EPD and switching transformation resolutions. The EPD helps in both coarse and fine techniques to implement object-constrained registration via controlling edges, which is not possible when using traditional similarity measures. Experiments are performed on clinical 3D magnetic resonance imaging (MRI) scans of the neck, with the results showing that the EPD is more effective than the mutual information (MI) and sum of squared difference (SSD) measure in terms of volumetric dice similarity coefficient (DSC). Additionally, the proposed method is compared with the diffeomorphic Demons and SyN state-of-the-art approaches with ablation studies in inter-subject deformable registration. The proposed method achieves better accuracy, robustness and consistency than the reference methods, with an average volumetric DSC of 0.7029 compared to 0.6654 and 0.6606 for the Demons and SyN methods, respectively.

Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering-based recommender systems

Recommender systems can improve the quality of life in smart cities by presenting personalized services to the community. Such systems maintain a database of user profiles for producing recommendations for a specific user. The collaborative filtering (CF) approach used in these systems has become a benchmark approach for generating recommendations for interested users because it can provide “out of the box” solutions. These CF-based approaches first construct a user–item rating matrix and then exploit similarity methods. These approaches suffer from scalability, sparsity, and cold user conditions, which consequently result in the poor recommendation accuracy of these systems. To enhance the accuracy of recommender systems, social trust can play a vital role because people tend to interact with a system or respond positively to recommendations that originate from their social trustworthy friends. The proposed unified approach of this article uses explicit trust, implicit trust, and user preference similarity to create a unified rating profile for the target user to produce more powerful and accurate recommendations. The proposed unified approach also enhances the recommendation performance of CF-based recommender systems when only a limited set of ratings is available. Experiments are performed on three publicly available datasets which are FilmTrust, CiaoDVD, and Epinions. Comparison of obtained results is made with traditional similarity measures as well as up-to-date trust-based approaches. The results show that the proposed unified approach is superior to existing approaches in terms of both predictive and classification-based accuracy measures.

Recognition of Herpes Viruses on the Basis of a New Metric for Protein Sequences

This paper addresses the problem of intellectual human herpes viruses recognition based on the analysis of their protein sequences. To compare proteins, we use a new dissimilarity measure based on finding an optimal sequence alignment. In the previous work, we proved that the proposed way of sequence comparison generates a measure that has properties of a metric. These properties allow for more convenient and effective use of the proposed measure in further analysis in contrast to the traditional similarity measure, such as Needleman-Wunch alignment. The results of herpes viruses recognition show, that the metric properties allow to improve the classification quality. In addition, in this paper, we adduce an updated computational scheme for the proposed metric, which allows to speed up the comparison of proteins.

Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems

One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.