Video Recommendation System Using Machine-Learning Techniques

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through learning.In this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different clients.The expectation depends on past evaluations,history,interest,IMDB rating,and so on.This can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific time.The required datasets for the video are taken from Grouplens.This recommender framework is executed by utilizing Python Programming Language.For building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the examples.For that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information focuses.The last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

With the rapid development of the Internet globally since the 21st century,the amount of data information has increased exponentially.Data helps improve people’s livelihood and working conditions,as well as learning efficiency.Therefore,data extraction,analysis,and processing have become a hot issue for people from all walks of life.Traditional recommendation algorithm still has some problems,such as inaccuracy,less diversity,and low performance.To solve these problems and improve the accuracy and variety of the recommendation algorithms,the research combines the convolutional neural networks(CNN)and the attention model to design a recommendation algorithm based on the neural network framework.Through the text convolutional network,the input layer in CNN has transformed into two channels:static ones and non-static ones.Meanwhile,the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher.The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding.It obtains data name features through a convolution kernel.Finally,the top pooling layer obtains the length vector.The attention system layer obtains the characteristics of the data type.Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage.The proposed algorithm shows excellent accuracy and robustness.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

A Content-Based Recommendation Framework for Judicial Cases

Under the background of the Judicial Reform of China, big data of judicial cases are widely used to solve the problem of judicial research. Similarity analysis of judicial cases is the basis of wisdom judicature. In view of the necessity of getting rid of the ineffective information and extracting useful rules and conditions from the descriptive document, the analysis of Chinese judicial cases with a certain format is a big challenge. Hence, we propose a method that focuses on producing recommendations that are based on the content of judicial cases. Considering the particularity of Chinese language, we use “jieba” text segmentation to preprocess the cases. In view of the lack of labels of user interest and behavior, the proposed method considers the content information via adopting TF-IDF combined with LDA topic model, as opposed to the traditional methods such as CF (Collaborative Filtering Recommendations). Users are recommended to compute cosine similarity of cases in the same topic. In the experiments, we evaluate the performance of the proposed model on a given dataset of nearly 200,000 judicial cases. The experimental result reveals when the number of topics is around 80, the proposed method gets the best performance.

Design of Hybrid Recommendation Algorithm in Online Shopping System

In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to users.Therefore,the accuracy of the recommendation algorithm is a primary issue.So far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation algorithms.Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings.The content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and scalability.On the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services.This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content set.The similarity gets the recommendation list.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.

Context-Aware Practice Problem Recommendation Using Learners’ Skill Level Navigation Patterns

The use of programming online judges(POJs)has risen dramatically in recent years,owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming.Since POJs have greater number of pro-gramming problems in their repository,learners experience information overload.Recommender systems are a common solution to information overload.Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners’current context,like learning goals and current skill level(topic knowledge and difficulty level).To overcome the issue,we propose a context-aware practice problem recommender system based on learners’skill level navigation patterns.Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and tofind learners’learning goals.Collaborativefiltering(CF)and con-tent-basedfiltering approaches are employed to recommend problems in the cur-rent and next skill levels based on frequent skill level navigation patterns.The sequence similarity measure is used tofind the top k neighbors based on the sequence of problems solved by the learners.The experiment results based on the real-world POJ dataset show that our approach considering the learners’cur-rent skill level and learning goals outperforms the other approaches in practice problem recommender systems.

An E-Commerce Recommender System Based on Content-Based Filtering

Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.

Adversarial Attacks on Content-Based Filtering Journal Recommender Systems

Recommender systems are very useful for people to explore what they really need.Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers.In order to improve the efficiency of selecting the most suitable journals for publishing their works,journal recommender systems(JRS)can automatically provide a small number of candidate journals based on key information such as the title and the abstract.However,users or journal owners may attack the system for their own purposes.In this paper,we discuss about the adversarial attacks against content-based filtering JRS.We propose both targeted attack method that makes some target journals appear more often in the system and non-targeted attack method that makes the system provide incorrect recommendations.We also conduct extensive experiments to validate the proposed methods.We hope this paper could help improve JRS by realizing the existence of such adversarial attacks.

An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature matching.In this paper,we extract the color features based on Global Color Histogram(GCH)and texture features based on Gray Level Co-occurrence Matrix(GLCM).In order to obtain the effective and representative features of the image,we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively.And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way.Image feature matching mainly depends on the similarity between two image feature vectors.In this paper,we propose a novel similarity measure method based on k-Nearest Neighbors(kNN)and fuzzy mathematical algorithm(SBkNNF).Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method.Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image.And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image.Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image.Extensive experiments are carried out on three data sets:WANG data set,Corel-5k data set and Corel-10k data set.The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.

Recommendation algorithm of cloud computing system based on random walk algorithm and collaborative filtering model

The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.

Predicting the CME arrival time based on the recommendation algorithm

CME is one of the important events in the sun-earth system as it can induce geomagnetic disturbance and an associated space environment effect.It is of special significance to predict whether CME will reach the Earth and when it will arrive.In this paper,we firstly built a new multiple association list for 215 different events with 18 characteristics including CME features,eruption region coordinates and solar wind parameters.Based on the CME list,we designed a novel model based on the principle of the recommendation algorithm to predict the arrival time of CMEs.According to the two commonly used calculation methods in the recommendation system,cosine distance and Euclidean distance,a controlled trial was carried out respectively.Every feature has been found to have its own appropriate weight.The error analysis indicates the result using the Euclidean distance similarity is much better than that using cosine distance similarity.The mean absolute error and root mean square error of test data in the Euclidean distance are 11.78 and 13.77 h,close to the average level of other CME models issued in the CME scoreboard,which verifies the effectiveness of the recommendation algorithm.This work gives a new endeavor using the recommendation algorithm,and is expected to induce other applications in space weather prediction.

Research and implementation of a personalized book recommendation algorithm --Taking the library of Jinan University as an example

Abstract： Taking the basic data and the log data of the various businesses of the automation integrated management system of the library in Jinan University as the research object this paper analyzes the internal relationship between books and between the books and the readers, and designs a personalized book recommendation algorithm, the BookSimValue, on the basis of the user collaborative filteringtechnology. The experimental results show that the recommended book information produced by this algorithm can effectively help the readers to solve the problem of the book information overload, which can bring great convenience to the readers and effectively save the time of the readers＇ selection of the books, thus effectively improving the utilization of the library resources and the service levels.

Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.

A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

Grid-based recommendation algorithms view users and items as abstract nodes,and the information utilised by the algorithm is hidden in the selection relationships between users and items.Although these relationships can be easily handled,much useful information is overlooked,resulting in a less accurate recommendation algorithm.The aim of this paper is to propose improvements on the standard substance diffusion algorithm,taking into account the influence of the user’s rating on the recommended item,adding a moderating factor,and optimising the initial resource allocation vector and resource transfer matrix in the recommendation algorithm.An average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation results.Experiments are conducted on the MovieLens training dataset,and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

Research on Recommendation Algorithms Based on Cloud Models in Probabilistic Linguistic Environments

To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language environment.Initially,this paper quantifies the attributes in the review text based on the probabilistic linguistic term set.Subsequently,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud generator.Finally,the products are recommended and sorted based on the digital characteristic value of the cloud model.The algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.