Content-based Recommendation Algorithm Research Articles

Research on movie recommendation algorithms based on machine learning

In recent years, the number of movies released worldwide has grown exponentially. Due to the large number of movies, it is difficult for users to find movies that match their preferences. Therefore, with the development of Internet technology, it has become an important research direction how to filter out the movies that users are interested in from the massive movie data. This paper mainly focuses on the film recommendation algorithms based on machine learning, including the traditional collaborative filtering algorithm, rating-based sorting recommendation algorithm and content-based recommendation algorithm. By conducting a detailed analysis of the principles, advantages and disadvantages, as well as application scenarios of these recommendation algorithms, this paper aims to identify methods that best fit current movie recommendation systems. The objective is to improve the real-time and personalized recommendation, while providing users with better film recommendation services.

Enhanced Content-Based Recommendation Using Topic Modelling and Knowledge Graph

Content-based (CB) recommendation algorithms recommend items to a user based on items the user liked in the past. CB methodologies have gained attention due to their higher accuracy and transparency and the emergence of new technologies, such as knowledge graphs (KGs), advances in natural language processing (NLP), and sentiment analysis. While most previous studies have mainly focussed on the use of term frequency-inverse document frequency (TF-IDF) and other related enhancements, little work can be found on using KGs in CB recommendations. This paper presents an enhancement of the conventional CB recommendation by incorporating KGs for a movie domain. The graph is constructed using the MovieLens data set, which is augmented with additional features such as actors, directors, and genres. Furthermore, the graph is expanded by incorporating topics derived from latent dirichlet allocation (LDA) extraction. Using the KGs, the proposed approach enhances user profiles by leveraging the interconnected user-movie relationship within a graph structure. The results of the experiments showed that the proposed approach exceeded the tested baselines in terms of precision, recall, and F-score metrics.

Comprehensive Travel Companion: A Hybrid Recommendation and Route Planning System for Globetrotting

This paper presents a comprehensive AI-powered travel companion system designed to enhance the travel experience for globetrotters. Leveraging cutting-edge artificial intelligence (AI) techniques, the system combines image recognition, recommendation algorithms, and route planning functionality to provide personalized travel recommendations and seamless navigation. The integration of collaborative filtering, content-based recommendation, and real-time route optimization algorithms ensures that users receive tailored suggestions and efficient travel routes based on their preferences, interests, and current context. The architecture, implementation details, and evaluation of the proposed system are discussed, highlighting its effectiveness in facilitating immersive and enjoyable travel experiences. Keywords: Travel Companion, Recommendation Systems, Route Planning, Image Recognition, Collaborative Filtering, Content-Based Recommendation, Dijkstra's Algorithm.

Research on the Design of Intelligent Recommendation System for Civic and Political Education Content and the Effectiveness Assessment of Students’ Acceptance

Abstract The article improves the traditional collaborative filtering algorithm, integrates it with the content-based recommendation algorithm, proposes the recommendation algorithm based on the mixture of collaborative filtering and content, and serves as the operation logic for designing the intelligent recommendation system of educational content for Civics class. Model variables are determined using structural equation modeling and relevant hypotheses are presented to construct a model of factors that influence student acceptance of the Civics Intelligent Recommendation System, followed by empirical analysis. The mean values of expectation performance, effort performance, social influence, convenience conditions, self-efficacy, perceived pleasantness, and willingness to use are 3.48, 2.70, 3.61, 2.36, 3.77, 3.84, and 3.73, respectively. Students’ use of the Civic Intelligent Recommendation System is greatly influenced by their perception of pleasantness and self-efficacy. The questionnaire has good reliability and validity in general. The initial model has valid hypotheses H1, H2, H6, H7, H8, H9, H10, and H11. In the analysis of variance, there were significant differences between genders only in performance expectations (0.000) and perceived pleasantness (0.016). Significant differences existed across grades in terms of performance expectations (0.018) and social influence (0.000). The measurement dimension of willingness to use had a moderating effect across majors. Hypotheses H12 and H13 are partially valid, but H14 is valid.

Promoting Educational Reform to Enhance Talent Cultivation Quality of Civil Engineering Majors Based on Deep Learning Background

Abstract This paper focuses on improving the quality of talent cultivation for civil engineering students through reforming teaching quality and course selection management. Regarding teaching quality management, the article proposes to use improved Apriori algorithm to generate high interest rules and utilize SQL for effective data manipulation. Regarding course selection management, the content-based recommendation algorithm is used to optimize the course selection mechanism. After the implementation of the reform, the proportion of low scores in the self-quality evaluation of college students was significantly reduced, and the evaluation indexes of teachers also showed significant differences. In addition, employers were satisfied with the competence of civil engineering graduates. These results indicate that data-driven teaching management and personalized course selection recommendation can effectively improve education quality and student satisfaction.

Exploring the Communication Path of Civic Education in Cyberspace Using Data Mining Technology

Abstract A recommendation algorithm is a specific application of data mining technology, which is an important means to expand the dissemination of Civics education in cyberspace. The elbow method is employed in this paper to determine the best number of clusters to cluster the Civics course evaluation dataset and finish preprocessing large-scale data. Aiming at the problem of different importance of rating bias between items, the traditional Slope One algorithm is optimized by comprehensively considering the relationship between the number of common ratings and the similarity between items and the prediction results. The optimized Slope One algorithm is used to perform one round of prediction and fill the scoring matrix to solve the data sparsity problem and improve prediction accuracy and stability. The optimized algorithm reduces by 8.56% compared to the user-based collaborative filtering algorithm, 5.83% compared to the item-based collaborative filtering algorithm, and 5.25% compared to the content-based recommendation algorithm, with a significant improvement in accuracy, recall, and F1 value. The four hypothesis validations indicate that the diversity, personalization, and accuracy of resource recommendations all have positive effects on the dissemination effect of civic education. The recommendation algorithm’s dissemination effect score in this paper is 3.781, which is significantly superior to the dissemination effect of Civic Education.

The Connotation of Objectives and Hierarchical Orientation of Teaching English and American Literature Based on the Era of Big Data

Abstract This paper combines the knowledge structure of English and American literature and learners’ personalized knowledge characteristics to design and propose personalized teaching based on a genetic algorithm. Genetic algorithms are used to extract learner characteristics, and content-based recommendation algorithms are applied to match ability characteristics, goal characteristics and learning object characteristics to achieve personalized course teaching. The crossover probability, variation probability, selection operator and crossover operator of the genetic algorithm are determined, and simulation experiments are designed to analyze the optimization effect of its parameters. Setting three levels of colleges and universities, with the connotation of the objectives and level positioning of English and American literature teaching, i.e., reading experience, reading knowledge, reading method, and the cultivation of cultural connotation and humanistic sentiment as factors, a one-way ANOVA was carried out to examine whether the teaching objectives of different levels of colleges and universities differed significantly in personalized teaching. In terms of the highest level of the teaching objectives of English and American literature, i.e., the cultivation of humanistic sentiment, F=6.607, Sig=0.002, 985 colleges>211 colleges and universities, and general undergraduate schools>211 colleges and universities.

ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content.Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.

Rural tourism service design based on collaborative filtering algorithm after epidemic normalization

Abstract In the epidemic, normalized tertiary and rural tourism service industries are in the economic depression stage. Under the epidemic, combined with big data technology to improve the economic income and development scale of rural tourism services become the current development trend of the tourism industry. This paper first proposes a collaborative filtering recommendation algorithm based on big data technology to study the design of rural tourism services under the new epidemic normalization. Then the basic principle of the content-based recommendation algorithm is to obtain the interests of tourists based on their historical behaviors and recommend rural tourism similar to their interest preferences, and choosing the appropriate similarity function can improve the accuracy of the neighborhood-based CF method. Finally, to meet the tourists’ demand for a full range of tourism experience services and build a rural tourism service system, the psychological demand of rural tourism tourists’ consumption is analyzed based on a collaborative recommendation algorithm. The results show that among the main factors attracting tourists, 75.54% are natural scenery, 54.68% are folk culture, 51.08% are unique flavors and food, 43.17 are experiencing rural life, and 41.73% are promoting relationships with friends. This study plays an important role in accelerating rural revitalization by attracting urban tourists back to the countryside and driving the transfer of consumer groups and the rural economy to increase income; thus, rural tourism plays an important role in accelerating rural revitalization.

Calculation and evaluation of similarity in sports information resources based on combined algorithm

Abstract It is helpful for athletes to obtain real-time and accurate sports information resources by integrating various recommendation algorithms into a personalised sports recommendation system. Therefore, this paper designs user-based and content-based recommendation algorithms, and recommends their results to sporters by weighted selection. In addition, by acquiring the characteristics of sporters, the method of the constraint space of core parameters is determined by using ontology rule reasoning, which ensures the rationality of sports parameter extraction, and thus establishes the similarity calculation model of sports resources and designs the strategy and method of model verification. Finally, the validity of the model is evaluated by Mahalanobis distance, and the recommendation effects of different algorithms are compared by similarity grade, which can provide sporters with personalised sports resources with stronger pertinence and better effect.

A hybrid recommendation algorithm for green food based on review text and review time

ABSTRACT Green food is well-known for its health benefits, environmental-friendliness, and safety. Current recommender systems used by e-commerce websites usually recommend products based on products' popularity or customers' ratings. However, users' reviews could be more representative of consumers' preferences. In addition, users' review time is not utilized. To reduce the recommendation bias, this study proposes a hybrid recommendation algorithm based on green food reviews and review time. The proposed algorithm combines a content-based recommendation algorithm with a user-based collaborative filtering approach, where affective values of reviews replace ratings and a time impact factor is considered. With the two classical evaluation indices of F1 and Mean Absolute Error (MAE), the experiments show that considering both reviews’ sentiments and dynamic changes of individuals’ preferences could improve recommendation effectiveness over three other algorithms, which provides a new reference direction for improving existing recommender systems on green food.

Operation and algorithm optimization of short video recommendation algorithm

Now that technology is developing rapidly, the field of computing has been showing exponential progress, and scientific and technological civilization of mankind is making continuous progress. Games, live broadcasts, and short videos are all new products under big data. Especially for short videos, the huge database records the preferences of billions of users. With the combination of collaborative filtering algorithms and content-based recommendation algorithms, computers can always accurately recommend suitable videos to various users. In this paper, improvement of this method is aimed at the item-based algorithm in the collaborative filtering algorithm. For the item-attribute matrix, first, use the Jaccard distance to calculate the similarity, and then use this similarity value instead of the Euler distance formula to bring it into the k-means clustering, and use iteration to obtain countless different clusters. Finally, set a threshold x, which is the distance between each cluster center. Whenever there is a new matrix to be classified, the similarity y corresponding to this matrix is calculated first. If y<x, the matrix is classified into the corresponding cluster. This approach can improve the diversity of recommended videos and tap the potential interests of users. Such improvements to the matrix can improve the accuracy of the algorithm and user stickiness.

Improved content recommendation algorithm integrating semantic information

Content-based recommendation technology is widely used in the field of e-commerce and education because of its intuitive and easy to explain advantages. However, due to the congenital defect of insufficient semantic analysis of TF-IDF vector space model, the traditional content-based recommendation technology has the problem of insufficient semantic analysis in item modeling, fails to consider the role of semantic information in knowledge expression and similarity calculation, and is not accurate enough in calculating item content similarity. The items with semantic relevance in content can not be well mined. The research goal of this paper is to improve the semantic analysis ability of the traditional content-based recommendation algorithm by integrating semantic information with TF-IDF vector space model for item modeling and similarity calculation and proposed an improved content recommendation algorithm integrating semantic information. In order to prove the effectiveness of the proposed method, several groups of experiments are carried out. The experiments results showed that the overall performance of the proposed algorithm in this paper is the best and relatively stable. This verified the validity of our method.

Online language education recommendation based on personalized learning and edge computing

The rapid development of the Internet has promoted the emergence of a large number of learning resources and online education platforms. However, massive resources and learning approaches have also led to the emergence of the information overload dilemma. This paper combines recommendation system and edge computing to implement personalized learning in online language education. First, the language learning resources are disposed from cloud centralized framework to edge‐cloud based framework. Second, the frequent language resources are selected by using a waterfall hybrid recommendation algorithm in which the language resources are roughly filtered by a collaborative filtering and then the roughly filtered resources are further processed by a content based recommendation algorithm to sort according to the similarity score. The experimental results demonstrate the effectiveness of the proposed personalized learning strategy.

DCF-MLSTM: a deep security content-based filtering scheme using multiplicative BiLSTM for movie recommendation system

Recently, the demand for online and offline recommendation systems has increased drastically. These systems are widely used in tourism, music, and video or movie recommendations. Currently, online movie streaming applications have gained huge attention. Providing better recommendations to the user is a challenging task for these applications. The content-based filtering (CBF) recommender system is a promising technique for these systems. However, traditional systems suffer from challenges such as cold-start problems, sparsity, and scalability. Consequently, we strengthen content-based recommendation algorithms by enriching the user-related and relevant product models with effective tendencies. The majority of previous work on classifiers has been in recommendation systems. To overcome these issues, we present a deep learning model that uses a deep neural network mechanism and a multiplicative BiLSTM model. This scheme uses embedding, weight updating, and preference learning processes to improve the recommendation system's performance. The performance of the proposed approach is measured in terms of MAE, MAP, Precision, Recall, and F-measure. The comparative performance shows that the proposed approach achieves better performance when compared with state-of-art movie recommendation techniques.

Application of Internet of Things technology in Human resource recommendation engine

In order to avoid the phenomenon of information overload in the field of human resources, the author proposes a human resource recommendation engine based on Internet of Things technology. Firstly, the application fields of the Internet of Things are introduced, and the human resource recommendation engine is designed, use a mixed referral approach in the HR field. We chose a hybrid recommendation strategy of PLSA and content based on the Internet of Things. The author introduces the human resource recommendation and content-based human resource recommendation of PLSA based on the Internet of Things respectively, on the basis of the above introduction, through the analysis of the existing hybrid recommendation strategy, a weighted hybrid method of PLSA based on the Internet of Things and content-based recommendation algorithm is proposed. In order to verify the feasibility of the proposed IoT-based PLSA and content-based HR recommendation method, we conducted a multi-step experiment. We selected 50 graduating students from A University to participate in this experiment, and the results show that in order to verify the overall effect of the algorithm, with accuracy and recall as evaluation criteria, we conducted experiments on recommendation algorithms of PLSA based on Internet of Things, content-based recommendation algorithms, and recommendation algorithms based on PLSA and content weighting mixture, where, the number of z in PLSA algorithm is 10, the value of β in mixed recommendation is 0.6, the size of N in TopN recommendation is N=5,10,20, and 30. Experiments were carried out on the accuracy and recall of the three recommendation algorithms, the number of z in PLSA algorithm is 10, the value of β is 0.6, and the size of N in TopN recommendation is N=3,5,7,9. The recommendation effect of PLSA based on the Internet of Things is better than that of content-based recommendation algorithm, and the recommendation effect of the combination of the two algorithms is better than either of them.

A Personalized Recommendation Algorithm for Semantic Classification of New Book Recommendation Services for University Libraries

With the rapid development of information technology and the Internet, it is difficult for university readers to find books of real interest or value from a large number of books by relying only on traditional retrieval-based services. This paper applies data mining technology and personalized recommendation algorithm based on semantic classification for new book recommendation service in university libraries. The personalized recommendation algorithm based on semantic classification establishes a book feature model and a reader preference model based on title keywords. The different recommendation strategies in the system framework are detailed. For the borrowing data of different colleges and departments, the improved association rule algorithm is used to mine the book association rules, and the reader’s borrowing history is matched with the association rules to generate a book recommendation list; according to the reader’s borrowing preference characteristics, the reader preference model is used as the basis. Class subdivision and then combined with the book feature model and reader preference model, the collaborative filtering recommendation algorithm and the content-based recommendation algorithm are applied to generate a book recommendation list. The active service method not only improves the service level of the university library, makes the development of the university library more comprehensive and humanized but also explores the potential information needs of readers, improves the borrowing rate of books in the collection, and maximizes the utilization rate of book resources. In the experiment of this paper, the personalized recommendation algorithm division of semantic classification is adopted. According to the division of its algorithm, the corpus is divided into 9603 training documents and 3299 test documents, with certain accuracy.

Application of Deep Learning Technology in the Recommendation System of Constitutional and Constitutional Cases

Due to the huge system in the field of constitution, the content of cases is complicated, and the number of documents related to constitutional cases is huge. It is incredibly tough to find the information that suits your demands in such a large volume of constitutional documents. The artificial neural network-based deep learning method has made significant progress. Deep learning has been used in the disciplines of computer image and video processing, as well as natural language processing, and has outperformed typical machine learning methods. To that purpose, the content-based recommendation algorithm in this work is improved using deep learning approaches. This paper primarily covers the following topics: (1) the current research status of deep learning technology in recommender systems is reviewed, and the theoretical knowledge of deep neural networks is introduced. (2) A text classification model OCCNN based on character-level convolutional neural network is proposed to solve the problem of document classification of massive cases. A semantic similarity calculation model TF-W2V based on word frequency and word vector is also proposed to solve the problem of semantic similarity calculation of cases. (3) The two methods are tested, and the experimental results show that the accuracy is much higher than that of the conventional method. The model can recommend cases that meet the needs of users according to the requirements put forward by users in the process of handling cases.

Personalized Item Recommendation Algorithm for Outdoor Sports.

With the rapid development of China's economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of “information overload,” and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it.

Application of E-Commerce Recommendation Algorithm in Consumer Preference Prediction

Through user characteristic information, user interaction behavior, commodity characteristic information, recommendation engine, and related technologies in data mining, this paper makes a more in-depth study, and analyzes the problems of "big data volume", "cold start" and "data sparsity" in the recommender system in modern business websites. In response to these problems, this paper transforms the problem of large data volume into the problem of large user groups. Then, after using the k-means clustering algorithm to divide the large user group into homogeneous user groups to alleviate the problem, a combination of collaborative filtering algorithm and content-based recommendation algorithm in the homogeneous user group is proposed to alleviate this problem. The experimental precision and recall are both around 0.4, and when W=0.8, the F value is the largest.