User-based Collaborative Filtering Algorithm Research Articles

Vehicle Fixing Recommender System Using User-Based Collaborative Filtering Algorithm

The study refers to vehicle fixing support, an Online Application System for vehicle owners with vehicular problems guided by features on recommending service providers according to their geographical location and performance rating of service providers using the Collaborative Filtering Algorithm. As the number of vehicle owners increases, the tendency for services related to vehicle fixing and maintenance could also increase, providing the chance for vehicle service providers to be in the limelight in the digital world. The system stakeholders include the vehicle owners, vehicle service providers, and the technical support team that administers the registration and approval process among vehicle owners and service providers. Data management with transactions established are properly kept to track and trace services availed by vehicle owners served as the basis for collaborative filtering. The Front-end and back- end of the system are structured using HTML5, Bootstrap 4, CodeIgniter4, JavaScript, and MySQL for its database. The system was found to have advertised the service providers’ skills and services to the customers as vehicle owners who search for and therefore book an appointment with the available service providers online. The desired results were met when the transaction is done from booking appointments through the system to successful services offered on-site. The search results had shown the recommended service providers that precisely fit the vehicle owner's or driver's search parameters criteria. The system's availability has been concluded which obtained a rating of 5 with the quality description of excellent functionality by the end-user reviews by the software requirements specifications

Personalised resource recommendation method for collaborative tagging system based on machine learning

In order to overcome the low feasibility of traditional resource recommendation methods, this paper proposes a personalised resource recommendation method based on machine learning. Firstly, the user-based collaborative filtering algorithm is used to calculate user personalised similarity, and then content-based collaborative filtering algorithm is used to calculate resource content similarity through cosine similarity. Combined with user similarity and resource content similarity, a hybrid computing model of resource similarity is established, and personalised recommendation is realised through statistical machine learning. The experimental results show that: the F-measure value of the method can reach 0.97, the coverage rate is maintained above 50%, the popularity is above 0.8, and the MAE value is always the minimum, and its precision is always higher than that of the contrast method. It shows that the proposed method can effectively improve the precision and feasibility of personalised recommendation results.

A similarity measure model based on the dissimilarity degree between users

This article offers a new users-similarity measure to improve recommender systems’ accuracy even when a few ratings are available. This model is based on the degree of dissimilarity between common ratings. On real data sets, we conducted our experiments to evaluate the proposed model’s accuracy. Experiments demonstrate the model’s capacity to locate a suitable neighborhood, resolve the user’s cold-start problem, and significantly improve recommendation accuracy. Thus, this research may be considered a supplement to previous studies on user cold-start and good neighbor selection difficulties in user-based collaborative filtering algorithms.

Satellite-to-ground communication decision system based on knowledge graph

The knowledge graph relies on the powerful knowledge aggregation ability to bring new space for progress to the satellite-to-ground communication decision-making system, and it performs better in accuracy, debugging, scalability and decoupling. Then, based on the user-based collaborative filtering algorithm, a collaborative filtering algorithm fused with path loss is proposed. Based on the path loss formula for the free propagation of radio waves, the similarity factor is derived, and the similarity calculation formula more in line with satellite-to-ground communication is obtained. Through the simulation results, it can be seen that the improved algorithm has improved accuracy and recall rate, resulting in more satisfactory recommendation results.

User-based Collaborative Filtering Algorithm Design and Implementation

With the rapid development of social humanities and technology, especially the development of the Internet, the current Internet era, the amount of information is increasing rapidly with explosive growth rate, the information overload problem is particularly obvious, on the accurate acquisition of information from the massive amount of information users want, from which the personalized recommendation technology was born. In order to solve the problem of acquiring the information users want, this paper researches and discusses a kind of personalized recommendation algorithm - a user-based collaborative filtering algorithm, analyzing the user behavior, comparing the advantages and disadvantages of other related algorithms, using the UserCF algorithm, and optimizing the sparse matrix to reduce the time and complexity of the operation. The algorithm is implemented by software to generate recommendation results. The results of the experimental data in this paper show that the algorithm is effective in recommending projects to users.

Research on diversity and accuracy of the recommendation system based on multi-objective optimization

As the information industry and the Internet develop rapidly, the use of big data enters people's vision and attracts attention. It makes the recommendation system come into being how to quickly extract the desired information from the excessive information. In the recommendation system, user-based collaborative filtering algorithm has become a research hotspot. Existing researches focus on improving collaborative filtering recommendation algorithm by using the kernel method, but still face the cold start problem, the diversity problem, the data sparsity problem, the concept drift problem and more others. To solve these problems, this paper proposes the user-based collaborative filtering based on kernel method and multi-objective optimization (MO-KUCF) which introduces kernel density estimation and multi-objective optimization. It can be increasing diversity of the recommendation systems, improving concept drift in dynamic data and the accuracy and diversity of the recommendation system. The dataset used in this article is the Netflix dataset. It analyzes the MO-KUCF algorithm with the user-based collaborative filtering (UCF) and user-based collaborative filtering based on kernel method (KUCF) by the mean absolute error (MAE). The MAE is compared with the internal user diversity $$I_{{\text{u}}}$$ index, and the pre-processed data set is divided into the training set and the test set, which are provided to the recommendation system for recommendation and evaluation. The results show that the accuracy of MO-KUCF improves by 5.6%, and the diversity also increases with decreasing values. Combining multi-objective optimization techniques with kernel density estimation methods can improve the diversity of recommendation systems effectively and solve the concept drift problem to achieve the purpose of improving system accuracy.

Collaborative filtering based recommendation of sampling methods for software defect prediction

The performance of software defect prediction have been hindered by the imbalanced nature of software defect data. Fortunately, a variety of sampling methods have been employed to improve defect prediction performance. However, researchers and practitioners are usually burdened with selecting the optimal sampling methods for the defect data at hand. In practice, no sampling method has been found to perform best in theory and practice. Therefore it is necessary and valuable to study how to select applicable sampling methods according to the current data characteristics. This paper presents a collaborative filtering based sampling methods recommendation algorithm (CFSR) for automatically recommending applicable sampling methods for the new defect data. CFSR firstly ranks existing sampling methods with historical defect data, and then mines the data similarity between the new and historical defect data with meta-features. Finally, all the information of ranked sampling methods and data similarity are combined to build a recommendation network, with which the user-based collaborative filtering algorithm is employed to recommend appropriate sampling methods for the new defect data. A thorough experiment with five classification algorithms, two prediction performance, five recommendation performance and 12 popular sampling methods was conducted over 20 imbalanced software defect data. The experimental results firstly demonstrate the importance and necessity of present study, and then show that the proposed CFSR method is feasible and effective.

A music recommendation algorithm based on clustering and latent factor model

The collaborative filtering recommendation algorithm is a technique for predicting items that a user may be interested in based on user history preferences. In the recommendation process of music data, it is often difficult to score music and the display score data for music is less, resulting in data sparseness. Meanwhile, implicit feedback data is more widely distributed than display score data, and relatively easy to collect, but implicit feedback data training efficiency is relatively low, usually lacking negative feedback. In order to effectively solve the above problems, we propose a music recommendation algorithm combining clustering and latent factor models. First, the user-music play record data is processed to generate a user-music matrix. The data is then analyzed using a latent factor probability model on the resulting matrix to obtain a user preference matrix U and a musical feature matrix V. On this basis, we use two K- means algorithms to perform user clustering and music clustering on two matrices. Finally, for the user preference matrix and the commodity feature matrix that complete the clustering, a user-based collaborative filtering algorithm is used for prediction. The experimental results show that the algorithm can reduce the running cost of large-scale data and improve the recommendation effect.

A Trust-Based Collaborative Filtering Approach to Design Recommender Systems

Collaborative Filtering (CF) is one of the most fre-quently used recommendation techniques to design recommender systems that improve accuracy in terms of recommendation, coverage, and rating prediction. Although CF is a well-established and popular algorithm, it suffers with issues like black-box recommendation, data sparsity, cold-start, and limited content problems that hamper its performance. Moreover, CF is fragile and it is not suitable to find similar users. The existing literatures on CF show that integrating users’ social information with a recommender system can handle the above-mentioned issues effectively. Recently, trustworthiness among users is considered as one such social information that has been successfully combined with CF to predict ratings of the unrated items. In this paper, we propose a trust-based recommender system, TrustRER, which integrates users’ trusts into an existing user-based CF algorithm for rating prediction. It uses both ratings and textual information of the items to generate a trust network for users and derives the trust scores. For trust score, we have defined three novel trust statements based on user rating values, emotion values, and re-view helpfulness votes. To generate a trust network, we have used trust propagation metrics to compute trust scores between those users who are not directly connected. The proposed TrustRER is experimentally evaluated over three datasets related to movie, music, and hotel and restaurant domains, and it performs significantly better in comparison to nine standard baselines and one state-of-the-art recommendation method. TrustRER is also able to effectively deal with the cold-start problem because it improves the rating prediction accuracy for cold-start users in comparison to baselines and state-of-the-art method.

Improved user-based collaborative filtering algorithm with topic model and time tag

Collaborative filtering algorithms make use of interaction rates between users and items for generating recommendations. Similarity among users is calculated based on rating mostly, without considering explicit properties of users involved. Considering the number of tags of a user can direct response the user preference to some extent, we propose a collaborative filtering algorithm using topic model called user-item-tag latent Dirichlet allocation (UITLDA) in this paper. UITLDA model consists of two parts. The first part is active user with its item. The second part is active user with its tag. We form topic model from these two parts respectively. The two topics constrain each other and integrate into a new topic distribution. This model not only increases the user's similarity, but also reduces the density of the matrix. In prediction computation, we also introduce time delay function to increase the precision. The experiments showed that the proposed algorithm achieved better performance compared with baseline on MovieLens datasets.

Application of Improved Collaborative Filtering Algorithm in Recommendation of Batik Products of Miao Nationality

Recommendation system has been widely used in various e-commerce websites, but there are few personalized recommendations for ethnic handicrafts. And the national handicraft recommendation belongs to the small batch recommendation behavior, so the recommended performance is limited by the sparsity of the data in the scoring matrix. To solve this problem, an improved collaborative filtering algorithm is proposed, in which the user-based collaborative filtering algorithm is nested in the collaborative filtering algorithm based on gradient descent. First, the scoring prediction value is filled into the unscoring items in the scoring matrix, and then the recommendation is given by using the collaborative filtering method based on gradient descent. The proposed method is applied to the recommendation of wax dyeing products of Miao nationality and compared with the existing traditional methods. The results show that the improved algorithm greatly improves the recommendation performance, reduces the recommendation error and improves the accuracy.

An Improved Collaborative Filtering Algorithms Based on Maximum Entropy Model and Markov Process to Mine User Behavior

Aiming at problems of data sparsity and poor interpretability of recommendation reasons in user-based collaborative filtering algorithm, an improved collaborative filtering algorithm based on maximum entropy model and markov process is proposed to mine user behavior habits. Firstly, the maximum entropy model is established through the user-behavior matrix of collaborative filtering algorithm, and the user behavior is fuzzily and preliminarily mined. Secondly, fuzzy behavior information obtained by the maximum entropy model is introduced as the initial state of markov process, and an optimization model is set up according to the internal relation of user-behavior matrix, thus reaching the state probability transition matrix. Once the markov process reaches a stable state, the user’s final behavior habit is excavated. Finally, this user behavior is integrated into the recommendation process, so that the interpretability of the recommendation is enhanced. The experimental results show that the improved collaborative filtering algorithm can promote the recommendation accuracy.

User-based collaborative filtering algorithm fusing the local and global nearest neighbour

Collaborative filtering method is one of the most successfully used recommendation algorithm. It can be divided into the global nearest neighbour based method and the local nearest neighbour based method. The global neatest neighbour based method has high stability but the recommended results are not sufficiently personalised. The local nearest neighbour based method is more accurate and personalised than the global neatest neighbour based method, but the prediction failure rate is high and it's not stable. Aiming at these problems, a new user-based collaborative filtering algorithm fusing the local and global nearest neighbour is proposed. Firstly, the max-min K-medoids clustering algorithm is applied for clustering items into several clusters. The local similarity of users is calculated in each item cluster. Secondly, the factor of correlation-weighted is introduced to improve the accuracy of the global similarity among users. Finally, this new similarity among users is presented to optimise the selection of neighbours of target user. This new method increases the recommendation precision while avoiding the shortcoming of instability. The experimental results on EachMovie and MovieLens-100K show that the prediction reliability and accuracy of this new method are better than traditional ones.

Research of Personalized Course Recommended Algorithm based on the Hybrid Recommendation

This paper presents a personalized course recommended algorithm based on the hybrid recommendation. The recommendation algorithm uses the improved NewApriori algorithm to implements the association rule recommendation, and the user-based collaborative filtering algorithm is the main part of the algorithm. The hybrid algorithm adds the weight to the recommendation result of the user-based collaborative filtering and association rule recommendation, implementing a hybrid recommendation algorithm based on both of them. It has solved the problem of data sparsity and cold-start partially and provides a academic reference for the design of high performance elective system. The experiment uses the student scores data of a college as the test set and analyzes results and recommended quality of personalized elective course. According to the results of the experimental results, the quality of the improved hybrid recommendation algorithm is better.

What and who with: A social approach to double-sided recommendation

Double-sided recommendations (DSR) have been recently introduced for an item and a group that the item is destined for. Herein we present an algorithm which takes inspiration from the Social Comparison Theory to recommend items that had an average positive evaluation from other users on the target user's social network. Other users' judgments are weighted according to the influence these users have on the target. Moreover, for each recommended item, we propose a group that encompasses all the target users' contacts who expressed a positive opinion on it.Our data show that users consider double-sided recommendations more useful than traditional recommendations which provide equivalent information. It was observed that our social DSR algorithm performs better in the event recommendation domain than a content-based one which has already been recognised as providing a good performance, in terms of precision, recall, accuracy and F1. This result is strengthened by our demonstrating that the good performance DSRs provide also depends on their peculiar structure and not only on the fact that they include social information. The item-recommendation part also performed better than a user-based collaborative filtering algorithm. Lastly, we found that users' scores for recommended item-group packages can be better predicted by considering only the system scores for the recommended groups, at least in the domain of social and cultural events. HighlightsDSR are suggestions made of an item and a group with whom that item can be consumed.We propose a DSR algorithm using information from the target user's social network.The DSR algorithm performs better than a user-based CF one in suggesting items.In the event domain our DSR algorithm outperforms a traditional, content-based one.Users seem to focus on the suggested group when assessing DSR in the event domain.

Collaborative Filtering Algorithm based on User in Cloud Computing

The user-based collaborative filtering algorithm has been widely used in various kinds of personalized recommendation systems. But it has a serious shortcoming: with the increasing number of the users and commodities, its calculation work grows rapidly. To address the problem of vast time consumption by big dataset, we utilize MapReduce programming idea to do parallelized transformation of the algorithm; finally deploy it to be run in Hadoop cloud computing platform. Experiments have revealed that if computing data is reasonably distributed and the data volume is big, then the algorithm performance of the algorithm can realize favorable linearly speeding effect.

Merging user and item based collaborative filtering to alleviate data sparsity

Memory based algorithms, generally referred as similarity based Collaborative Filtering (CF) algorithm, is one of the most widely accepted approaches to provide service recommendations. It provides personalized and automated suggestions to customers to select variety of products. Memory based algorithms mainly have two kinds of algorithms: User-based and Item-based algorithms. The User-based CF algorithm recommends items by finding similar users. Contrary to User-based CF, an Item-based CF algorithm recommends items by finding similar items. The core of memory based CF technologies is to calculate similarity among users or items. However, due to inherent sparsity, a large number of entries (ratings) in user-item rating matrix are missing. This results in only few available ratings to make prediction for the unknown ratings. This results in poor prediction quality of the CF algorithm. In this paper a hybrid approach is presented that combines user-based CF and item-based CF. It also leverage the biclustering technique to reduce the dimensionality. The biclustering helps to cluster all users/items into several groups. These clusters are then used to measure users/items similarities based on their respective parent groups. To obtain individual prediction, it adopts the user-based and item-based CF schemes based on the computed similarity respectively. Finally it combines the resultant predictions of each model to make final predictions. Interestingly, experiments demonstrated that the proposed approach outperforms the traditional user-based, item-based and some state of the art recommendation approaches in terms of accuracy of prediction and quality of recommendations.

AUI&GIV: Recommendation with Asymmetric User Influence and Global Importance Value.

The user-based collaborative filtering (CF) algorithm is one of the most popular approaches for making recommendation. Despite its success, the traditional user-based CF algorithm suffers one serious problem that it only measures the influence between two users based on their symmetric similarities calculated by their consumption histories. It means that, for a pair of users, the influences on each other are the same, which however may not be true. Intuitively, an expert may have an impact on a novice user but a novice user may not affect an expert at all. Besides, each user may possess a global importance factor that affects his/her influence to the remaining users. To this end, in this paper, we propose an asymmetric user influence model to measure the directed influence between two users and adopt the PageRank algorithm to calculate the global importance value of each user. And then the directed influence values and the global importance values are integrated to deduce the final influence values between two users. Finally, we use the final influence values to improve the performance of the traditional user-based CF algorithm. Extensive experiments have been conducted, the results of which have confirmed that both the asymmetric user influence model and global importance value play key roles in improving recommendation accuracy, and hence the proposed method significantly outperforms the existing recommendation algorithms, in particular the user-based CF algorithm on the datasets of high rating density.

Research on Improved Collaborative Filtering Recommendation Algorithm on MapReduce

Information overload is one of the most serious problems in big data environment, recommendation systems is a way to effectively mitigate the problem. In order to make use of rich user feedback and social networks information and to further improve the performance of the recommendation system ,This thesis makes a improvement on the user-based collaborative filtering algorithm by normalization method, Meanwhile the algorithm could be run on the MapReduce in the Hadoop platform. The experimental results show that the algorithm on Hadoop platform can effectively improve the accuracy of the data to recommend and computational efficiency, so as to improve the satisfaction of users.

KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications

Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly improves the accuracy and scalability of service recommender systems over existing approaches.