Collaborative filtering over evolution provenance data for interactive visual data exploration

Lahmar, Houssem Ben; Herschel, Melanie

doi:10.1016/j.is.2020.101620

Cited by 3 publications

(13 citation statements)

References 21 publications

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“…By analyzing the data in Figure 2, it can be seen that the recommended recall rate of the recommendation algorithm based on optimized clustering is 0.42 at the lowest when the number of negative samples is 1 and then increases with the increase of the number of negative samples, with the highest recommended recall rate of 0.62. The highest recall rate of the method in literature [6] is 0.44, and the highest recall rate of the method in literature [7] is 0.44, which is lower than that of this method, which proves that the recommendation effect of the recommendation algorithm based on optimized clustering proposed in this paper is more accurate.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 73%

“…The number of target recommended contents is set to 500. The methods of literature [6], literature [7], and this paper are used to recommend to the target group, respectively. The completion time of 500 recommended contents by the three methods is counted to verify the complexity of different algorithms, as shown in Figure 3.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…In addition, 100458 comment records and behavior records were imported into the experimental environment. Upload all the prepared resource data samples to the experimental environment, and select the methods of literature [6] and literature [7] as the comparison method for the following tests. In order to ensure the accuracy of recommendation ranking of recommendation algorithm, it is necessary to measure the correlation between recommendation elements and judge the ranking quality of recommendation algorithm according to the position of recommendation results, so as to obtain the index to measure the recall rate of recommendation algorithm.…”

Section: Preparation Of Mental Health Data Samplesmentioning

confidence: 99%

“…In order to improve the retrieval speed of information related to the mental health of users, scholars not only recommend the methods of searching information in the network, but also solve the problems of mental health of users [2,3]. At this stage, the recommendation methods of college mental health teaching materials at home and abroad can be divided into content-based filtering [4], collaborative filtering [5,6], and rule-based recommendation methods [7] according to the input data and composition structure. However, the above traditional resource recommendation methods are mainly aimed at static mental health data stored in solid-state database, which will lead to poor recommendation effect and slow recommendation speed [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

Zhou

2022

Wireless Communications and Mobile Computing

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In order to meet the differentiated needs of students and improve the satisfaction of college mental health textbook recommendation, a college mental health textbook recommendation scheme based on improved deep learning algorithm is proposed. Based on the analysis of the principle of deep learning data recommendation system, the data interest is calculated according to the browsing records of students on college mental health textbooks. Combine the deep learning algorithm and collaborative filtering algorithm to collect the demand data of college mental health textbooks, then use the Naive Bayesian classification method to divide the college mental health textbooks into interested and uninterested parts, and recommend the interested college mental health textbooks to the students in need. Experiments show that the longest recommendation time of the college mental health textbook recommendation scheme based on the improved deep learning algorithm proposed in this paper is 4.5 min, the highest recommended recall rate is 95.18%, the average accuracy is 97.2%, the highest content richness is 0.8, the system stability coefficient is 1.06, and the overall average praise rate is 97.79%. It has a good recommendation effect.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 73%

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Preparation Of Mental Health Data Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

Zhou

2022

Wireless Communications and Mobile Computing

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“…Our main contribution, which derived by analyzing the results and findings of the reviewed papers in this report, will be the involvement and usage of the Naïve Bayes classification technique among a certain dataset, which has proved as a feasible approach, based on the preliminary results of an ongoing study that our research group is involved. Further, by analyzing publications covered in this literature survey the following are the main findings: − The content-based filtering (CBF) approach remains the most widely used, in which the items that are recommended are similar to what the user knows (using Similarity), and as emphasized in [58] where collaborative filtering is combined with content-based systems to improve the recommendation results overall. Since the number of items is high while the number of users is low and very few of the users rate the same items, CBF is used to cover cold start and sparsity problem, although CBF also has its limits.…”

Section: Findings and Correlationmentioning

confidence: 99%

Provenance and social network analysis for recommender systems: a literature review

Rrmoku

Selimi²,

Ahmedi³

2022

IJECE

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<span>Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network.</span>

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Research on Personalized Recommendation Method of Intangible Social Heritage and Materials in Schools under Double Reduction Policy

Jiang

Lingchun

2022

Mobile Information Systems

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Individualized recommendation of imperceptible social heritage and materials in schools under the double reduction policy is to evaluate and judge students’ learning interests and specialties and recommend suitable imperceptible social heritage and materials to students. Aiming at the problem that the collaborative filtering recommendation method does not match the individualized needs of primary and secondary school students under the double reduction policy, in this paper, we suggest a personalized recommendation system of imperceptible social heritage and materials in schools under the double reduction policy based on joint template feature matching and interest feature point mining. First, taking the information management platform of imperceptible social heritage and materials in schools as the structural model, the grading model and homomorphic distribution attribute model of imperceptible social heritage and materials for primary and secondary school students under the double reduction policy are constructed. Second, the probability density characteristic analysis method of joint template matching is used to construct the personalized recommendation model of the imperceptible social heritage and materials in schools. Third, then the personalized characteristic distribution and fitness parameter extraction of the imperceptible social heritage and materials in schools under the double reduction policy are carried out, so as to realize the reasonable matching of personalized characteristic requirements and project interest points and realize the personalized recommendation of imperceptible social heritage and materials in schools under the double reduction policy. Finally, a simulation experiment was carried out to test and evaluate the outcomes. The outcomes express that the personalized recommendation items of imperceptible social heritage and materials in schools with this method have higher scores, and the average absolute error and root mean square error are smaller, which improves the quality of dynamic and accurate matching between imperceptible social heritage and materials and students’ hobby characteristics.

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Collaborative filtering over evolution provenance data for interactive visual data exploration

Cited by 3 publications

References 21 publications

Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

Research on Recommendation of College Mental Health Teaching Materials Based on Improved Deep Learning Algorithm

Provenance and social network analysis for recommender systems: a literature review

Research on Personalized Recommendation Method of Intangible Social Heritage and Materials in Schools under Double Reduction Policy

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