Video Recommendation Algorithm Research Articles

Research and application analysis of typical recommendation methods

Recommendation systems are vital tools in the modern digital landscape for handling vast amounts of online data and are now employed across a variety of sectors. This manuscript underlines the importance of video recommendation algorithms and delves into the three main types: content-based, collaborative filtering, and hybrid algorithms. Content-based algorithms offer suggestions by assessing and matching the descriptive attributes of videos. These attributes could range from the genre, the actors involved, the director, or any other related metadata. On the other hand, collaborative filtering algorithms provide recommendations by comparing the user's historical data with that of others. They function on the principle that individuals who have agreed in the past are likely to agree again in the future. Hybrid recommendation algorithms, as the name suggests, are a combination of both content-based and collaborative filtering algorithms. They aim to harness the strengths of the two while mitigating their individual weaknesses, providing more balanced and accurate recommendations. This detailed exploration of these algorithms not only underscores their significance but also paves the way for the future, heralding the potential advancements and adaptations these systems could undergo. Continued innovation in this realm has the potential to revolutionize how information is processed and shared, ultimately enriching the user experience across the digital platform landscape.

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.

Research on Intelligent English Education Based on the Short Video Recommendation Algorithm.

In order to solve the problems of English education in the form of a short video, a research method of English intelligent education based on a short video recommendation algorithm was proposed. The recommendation system is a branch of artificial intelligence data mining, which improves the efficiency of short videos for English learning. The density ratio of users and video scoring matrix was 1000000/(1030 × 9394) = 10.3%. The dataset was a relatively sparse matrix. The original dataset was randomly divided into the training set and the test set, accounting for 80% and 20%, respectively. Then, the results of the short video recommendation algorithm were elaborated based on time weighting. Finally, the intelligent initial question bank of English intelligent education based on a short video recommendation algorithm was elaborated, which provided a guarantee for the promotion of short videos in English education.

A Heuristic Video Recommendation Algorithm based on Similarity Computation for Multiple Features Analysis

Objective:: The short video applications have achieved great success in recent years. The number of videos being shot and uploaded to these platforms has increased greatly. In this way, mining and recommending videos for users based on their interests have become a very difficult problem in these video distribution platforms. Under this case, it becomes particularly important to design efficient video recommendation algorithms for these platforms. In order to solve the problem faced by high sparsity and large scale data sets in the field of media big data mining and recommendation, a heuristic video recommendation algorithm for multi-dimensional feature analysis and filtering is proposed. Methods:: Firstly, the video features are extracted from multiple dimensions such as user behavior and video tags. Then, the similarity analysis is carried out, and the video similarity degree is calculated by weighting, so as to obtain the similar video candidate set, and filter the similar video candidate set. After that, the videos with the highest scores are recommended to users by sorting. Finally, the video recommendation algorithm proposed in this paper is implemented by using the C language. Results:: Compared with the benchmark, the proposed video recommendation algorithm has improved the accuracy by 6.1%-136.4%, the recall rate by 19.3%-30.9%, the coverage rate by 55.6%-59.5%, the running time by 42.7%-60.4%, and the cache hit ratio by 10.9%-47.4%. Conclusion:: The proposed algorithm can effectively and greatly improve the accuracy, recall rate, coverage rate, running time and cache hit ratio.

Analysis of College Art Teaching System under the Background of Video Big Data Technology

Art teaching needs not only learning art knowledge but also a lot of practice and aesthetic appreciation. However, traditional teaching methods cannot provide students with a large number of relevant learning materials, which is not conducive to improving students’ classroom enthusiasm. This paper presents the design and implementation of college art teaching system based on video big data technology and combines video recommendation algorithm with the Django teaching video website. The system analyzes the preference needs according to the behavior data of students watching videos and recommends videos for students. At the same time, the system can also evaluate the quality of the video content according to the behavior of students watching videos, reverse classify the video, and then optimize the recommendation results. The video recommendation algorithm model based on user behavior is better than the traditional collaborative filtering recommendation algorithm and fully connected neural network collaborative filtering algorithm. It can reduce the range of users who need similarity calculation and improve the accuracy of recommendation algorithm. The experimental results show that the fully connected neural network collaborative filtering algorithm has good recommendation performance and stability, can reduce the computational complexity, and can improve the recommendation accuracy. The teaching technology integrated through the Internet can greatly improve students’ enthusiasm for art teaching.

The role of non-elites and eyewitness videos in the visual securitisation of Calais asylum seekers

AbstractWith the advent of smartphones and social media, non-elites possess more resources to engage in politicisation and securitisation of issues. The increasing popularity and acceptance of eyewitness videos captured by non-elites presents new political-security implications, especially for the issue of migration and refugees, as witnessed during the European refugee crisis. The eyewitness videos the host population of Europe captured served as key heuristic artefacts for non-elites to engage in the securitisation and counter desecuritisation of asylum seekers. The production, visual, circulation, and audiencing aspects of eyewitness videos are significantly different from those of professional videos from news media and therefore can potentially articulate (in)security in distinct ways. I argue that the epistemic-political constitution of eyewitness videos and their online remediation through the complex networked act between the calculated security publics and video recommendation algorithms of social media play a key role in facilitating security articulations of asylum seekers. This is illustrated in the analysis of two popular eyewitness videos from Calais. The article examines how non-elites used eyewitness videos to politicise and securitise Calais asylum seekers.

THE ALGORITHM PLAYGROUND: A CONTENT ANALYSIS OF USER-PRODUCED CHILDREN’S VIDEOS ON YOUTUBE

With early-childhood mobile media device use on the rise, online video content plays an ever-increasing role in children’s lives. Of the wide variety of content available to children, user-produced videos on YouTube seem to be most popular. However, due to the platform’s size and the overwhelming number of child-targeted videos found on YouTube, scholars have been struggling with how to approach and study this topic. This study aims to address the gap in research by analyzing prevalent user-produced children’s videos on YouTube, with research questions focusing on video genres, their features, and content themes. Drawing on YouTube’s popularity-measurements and video recommendation algorithm, a corpus of 100 user-produced videos targeted to children was assembled. A content analysis of these videos led to the identification and conceptualization of 13 distinct genres of user-produced children’s videos: unboxing, surprise eggs, finger family, play-doh, nursery rhymes, kids songs, learning, pretend play (enactment), pretend play (toys), storytelling, arts & crafts, entertainer in character, and process repetition. Furthermore, the findings indicate that there are often unique interplays between genre type and the content, the production format, and the overall quality and educational rating. In addition to shedding light on the importance of studying child-targeted content on YouTube, this study’s main contribution is a typological map of the user-produced children’s video ecosystem that future studies from various fields can draw on.

An Empirical Study on Filter Bubbles in the YouTube Comments Network

Some point out that the influence of YouTube's video recommendation algorithm is causing users to be exposed to only video clips in limited subjects or fields, especially to biased content with opinions that are tilted to one side. However, there is a lack of empirical research on filter bubbles as algorithms in YouTube have not been disclosed. This study indirectly demonstrated the phenomenon of filter bubble on YouTube by extracting comment-based content network between uploaders who posted videos and writers who wrote comments on the video by each subject of the contents. Also, this study analyzed communication patterns between users through social network analysis (SNA). According to the analysis, users' narrow information acquisition and communication phenomenon caused by the filter bubble in YouTube was found.

유튜브 성장 분석: 유튜브 성장 촉진과 억제의 인과순환구조 분석을 중심으로

This study attempts to identify the structure in which YouTube has been able to grow rapidly. Also, this study seeks to find out the structure that constrains YouTube’s growth. Several entities were involved in YouTube’s growth, so a causal map analysis was used to consider them as a system. The results are as follows. Five partial causal maps and one comprehensive causal map related to YouTube are drawn. A few implications are derived from these causal analysis. First, YouTube’s credibility plays an important role in YouTube growth. Second, the discrimination ability of YouTube algorithms should be improved quickly. Third, if YouTube constrains a YouTuber, detailed explanations are needed on why and how to solve it. Fourth, it is also necessary to consider increasing reliability by improving video recommendation algorithm that only pursue advertising revenue currently. Fifth, YouTube needs to increase the subscription service ratio. Sixth, improving the mobile interface is required to maintain viewing time of YouTube.

Video Recommendation Algorithm based on Knowledge Graph and Collaborative Filtering

Video Recommendation Algorithm based on Knowledge Graph and Collaborative Filtering

CMBPR: Category-Aided Multi-Channel Bayesian Personalized Ranking for Short Video Recommendation

Due to the viewing convenience for social media users' fragmented time, the short video has become a new carrier for users' network demands on information spread, news reading, social contact, entertainment, and leisure. Therefore, short video recommendation is one of the most important research topics in social media. Current short video recommendation algorithms mainly focus on detecting user's social attributes, developing cross-domain information and so on, few researchers combine video category information and multi-behavior information together. This paper proposes a content-based recommendation algorithm Category-aided Multi-channel Bayesian Personalized Ranking (CMBPR) for short video recommendation, which integrates users' rich preference information by considering the difference among both different video categories and different user interactions. The experimental results demonstrate the effectiveness of the CMBPR video recommendation algorithm, which achieves a significantly higher recommendation accuracy than the traditional video recommendation algorithms and solves the influence of the “Long Tail” effect.

A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map

Learning resource recommendation, such as curriculum video recommendation, is an effective way to reduce cognitive overload in online learning. The existing curriculum video recommendation systems are generally limited to one course, ignoring the knowledge correlation between courses. In this paper, we propose a two-stage cross-curriculum video recommendation algorithm that considers both the learners’ implicit feedback and the knowledge association between course videos. First, we use collaborative filtering to generate a video seed set, which is based on the learner’s implicit video feedback, such as video learning frequencies, video learning duration, and video pausing and dragging frequencies. Second, we construct a cross-curriculum video-associated knowledge map and use a random walk algorithm to measure the relevance of the course videos. The relevance is based on each video seed as a starting node and is extended to a video subgraph. Then, several cross-curricular video-oriented subgraphs are recommended for the learners. The experimental results indicate that our cross-curriculum video recommendation algorithm performs better than the traditional collaborative filtering-based recommendation algorithms in terms of accuracy, recall rate, and knowledge relevance.

A Video Recommendation Algorithm Based on Hyperlink-Graph Model

The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (<scp>BDS‐IDC</scp>)

A video recommendation algorithm based on the combination of video content and social network

SummaryRecently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Exploring A Trust Based Recommendation Approach for Videos in Online Social Network

With the rapid development of social network, more and more users watch videos through social network, such as Sina Weibo. Traditional video recommendation algorithms aim at online video systems an...

A content and user-oblivious video-recommendation algorithm

Videos have become popular on Internet, and corresponding video-recommendation algorithms have become an important factor for maintaining user’s satisfactory level and the profits of video-service providers. Existing recommendation algorithms are often highly dependent on the precious information of contents and users. However in many scenarios those information is not available for privacy reasons. This paper provides a watching sequence-based video-recommendation algorithm that can work well without the video contents and users information. The algorithm consists of three models: the exactly prefix matching tree, partial prefix matching tree, and the postfix matching tree. The final recommendation results are composed from the three models. The corresponding search tree, the matching search tree, and weight calculating algorithm are developed for each model. The algorithm is evaluated based on the half-year log files of a practical video website. The experimental results show that our algorithm performs better on execution time, accuracy, diversity of recommendation results, and non-hot coverage than the traditional recommendation algorithms.