Video Recommendation Research Articles

Effect of metacognitive strategies and verbal-imagery cognitive style on biology-based video search and learning performance

Videos have diverse content that can assist students in learning. However, because videos are linear media, video users may take a longer time than readers of text to evaluate the context. Therefore, the process of video search may vary from one user to another depending on the users' individual characteristics, and the effectiveness of video learning may also vary across individuals. This study evaluated 100 Taiwanese fifth graders searching for videos related to “understanding animals” on YouTube and examined the effects of the students' metacognitive strategies (planning, monitoring, and evaluating) and verbal-imagery cognitive style on their video searches. The observable indicators were quantitative (search behaviors, search performance, and learning performance) and qualitative (search process observations and interviews). The study concludes that metacognitive strategy is the primary influencer of video search. Students with better metacognitive skills used fewer keywords, browsed fewer videos, and spent less time evaluating videos, but they achieved higher learning performance. They reviewed the video metadata information on the user interface and did not attempt to watch videos on the video recommendation lists, particularly videos that were irrelevant to the task requirements. During the course of the searches, keyword usage had a significant influence on the students' search performance and learning performance. The fewer keywords the students used, the better search and learning performance they were able to achieve. Our results are different from those of previous studies on text, image, and map searches. Accordingly, users must adopt different search strategies when using various types of search engines.

Detection of Spammer Based On the UserRecommendation Report in Web Mining

Online video sharing systems, out of that YouTube is that the most well-liked, offer options that permit users to post a video as a response to a discussion topic. These options open opportunities for users to introduce impure content, or just pollution, into the system. Therefore we discover for example, spammers could post associate unrelated video as response to a well-liked one, aiming at increasing the chance of the response being viewed by a bigger range of users. We have a tendency to propose the users Video Recommendation (UVR) system in cloud computing atmosphere. Video attributes capture specific properties of the videos uploaded by the supplier We employing a novel rule to as ALAC (active lazy associative classifier).Content pollution could jeopardize the trust of users on the system we offer a characterization of content, individual, and social attributes that facilitate distinguish every user category. Classification approach succeeds at separating spammers and promoters video search systems is fooled by malicious attacks that depends on a good selective sampling strategy to traumatize the foremost favorite Videos. This work provides a high flexibility, high reliability, low-level transparency, security features. Proposed tag cloud recommendation approaches.

사용자의 소셜 카테고리를 이용한 유튜브 동영상 추천 알고리즘

With the rapid progression of the Internet and smartphones, YouTube has grown significantly as a social media sharing site and has become popular all around the world. As users share videos through YouTube, social data are created and users look for video recommendations related to their interests. In this paper, we extract users' social category based on their social relationship and social category classification list using YouTube data. We propose the YouTube recommendation algorithm using the extracted users' social category for more accurate and meaningful recommendations. We show experiment results of its validation.

VecLP: A Realtime Video Recommendation System for Live TV Programs

We propose VecLP, a novel Internet Video recommendation system working for Live TV Programs in this paper. Given little information on the live TV programs, our proposed VecLP system can effectively collect necessary information on both the programs and the subscribers as well as a large volume of related online videos, and then recommend the relevant Internet videos to the subscribers. For that, the key frames are firstly detected from the live TV programs, and then visual and textual features are extracted from these frames to enhance the understanding of the TV broadcasts. Furthermore, by utilizing the subscribers' profiles and their social relationships, a user preference model is constructed, which greatly improves the diversity of the recommendations in our system. The subscriber's browsing history is also recorded and used to make a further personalized recommendation. This work also illustrates how our proposed VecLP system makes it happen. Finally, we dispose some sort of new recommendation strategies in use at the system to meet special needs from diverse live TV programs and throw light upon how to fuse these strategies.

Twitter is Faster

Traditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages of the proposed method.

A Survey on Video Recommendation and Ranking in Video Search Engine

A Survey on Video Recommendation and Ranking in Video Search Engine

An Effective Program Clustering Algorithm for TV Recommendation System

As the increasing popularity of smart TV, providing interactive and personalized TV video recommendation to TV viewers as on internet has been an urgent requirements for TV operators. To recommend a program to viewer satisfying individual interest, the classification of TV programs will be necessary, which should reflect the users' interests. This paper presents an approach for clustering TV programs with using users' VoD(Video on Demand) data. After analyzing the VoD data source, the VoD duration ratio is proposed as the measure of user's interest to a TV program. Through analyzing clustering result, it is observed of the implicit relation of user interest and we explained the relation.

VideoTopic

With the vast amount of video data uploaded to the Internet every day, how to analyze user interests and recommend videos that they are potentially interested in is a big challenge. Most video recommender systems limit the content to metadata associated with videos, which could lead to poor recommendation results since the metadata is not always available or correct. On the other side, visual content of videos contain information of different granularities, from a whole video, to portions of a video, and to an object in a video, which are not fully explored. This extra information is especially important for recommending new items when no user profile is available. In this paper, a novel recommendation framework, called VideoTopic, that targets at cold-start items is proposed. VideoTopic focuses on user interest modeling and decomposes the recommendation process into interest representation, interest discovery, and recommendation generation. It aims to model user interests by using a topic model to represent the interests in the videos and then discover user interests from user watch histories. A personalized list is generated to maximize the recommendation accuracy by finding the videos that most fit the user's interests under the constraints of some criteria. The optimal solution and a practical system of VideoTopic are presented. Experiments on a public benchmark data set demonstrate the promising results of VideoTopic.

Supporting exploratory video retrieval tasks with grouping and recommendation

In this paper, we present ViGOR (Video Grouping, Organisation and Recommendation), an exploratory video retrieval system. Exploratory video retrieval tasks are hampered by the lack of semantics associated to video and the overwhelming amount of video items stored in these types of collections (e.g. YouTube, MSN video, etc.). In order to help facilitate these exploratory video search tasks we present a system that utilises two complementary approaches: the first a new search paradigm that allows the semantic grouping of videos and the second the exploitation of past usage history in order to provide video recommendations. We present two types of recommendation techniques adapted to the grouping search paradigm: the first is a global recommendation, which couples the multi-faceted nature of explorative video retrieval tasks with the current user need of information in order to provide recommendations, and second is a local recommendation, which exploits the organisational features of ViGOR in order to provide more localised recommendations based on a specific aspect of the user task. Two user evaluations were carried out in order to (1) validate the new search paradigm provided by ViGOR, characterised by the grouping functionalities and (2) evaluate the usefulness of the proposed recommendation approaches when integrated into ViGOR. The results of our evaluations show (1) that the grouping, organisational and recommendation functionalities can result in an improvement in the users’ search performance without adversely impacting their perceptions of the system and (2) that both recommendation approaches are relevant to the users at different stages of their search, showing the importance of using multi-faceted recommendations for video retrieval systems and also illustrating the many uses of collaborative recommendations for exploratory video search tasks.

Personalized Video Recommendation through Graph Propagation

The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest. However, the vast quantity of videos also turns the finding process into a difficult task. In this article, we address the problem of providing personalized video recommendation for users. Rather than only exploring the user-video bipartite graph that is formulated using click information, we first combine the clicks and queries information to build a tripartite graph. In the tripartite graph, the query nodes act as bridges to connect user nodes and video nodes. Then, to further enrich the connections between users and videos, three subgraphs between the same kinds of nodes are added to the tripartite graph by exploring content-based information (video tags and textual queries). We propose an iterative propagation algorithm over the enhanced graph to compute the preference information of each user. Experiments conducted on a dataset with 1,369 users, 8,765 queries, and 17,712 videos collected from a commercial video search engine demonstrate the effectiveness of the proposed method.

Video recommendation based on multi-modal information and multiple kernel

Collaborative Filter (CF) algorithms often suffer from data sparsity and item cold start problem, for the user-item matrix is insufficient and extremely sparse especially when new item is added to recommendation system. These two problems also exist in video recommendation process. We propose two methods to solve them by incorporating multimodal information and multiple kernel together. To solve item cold start problem, we learn a user taste hyper-plane by using multiple kernel SVM to represent the user taste, which is further used to predict the recommendation of new added videos. We combine the different user taste hyper-plane similarity and the traditional cosine similarity with a trade-off between them to address the data sparse problem. Experimental results show that our proposed algorithm can alleviate the data sparsity and item cold start problems.

Cloud based real-time collaborative filtering for item–item recommendations

We describe a large scale implementation of a video recommendation system in use by the largest media group in Latin America. Taking advantage of existing recommendation system techniques, the proposed architecture goes beyond the state of the art by making use of a commercial cloud computing platform to provide scalability, reduce costs and, more importantly, response times. We discuss the implementation in detail, in particular the design of cloud based features. We also provide a comprehensive generalization of the architecture that allows its application in other settings.

Near-duplicate video retrieval

The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of an already large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted a lot of attention. As discovered in recent works, latest improvements and progress in near-duplicate video retrieval, as well as related topics including low-level feature extraction, signature generation, and high-dimensional indexing, are employed to assist the process. As we survey the works in near-duplicate video retrieval, we comparatively investigate existing variants of the definition of near-duplicate video, describe a generic framework, summarize state-of-the-art practices, and explore the emerging trends in this research topic.

Sharing Video Emotional Information in the Web

Video growth over the Internet changed the way users search, browse and view video content. Watching movies over the Internet is increasing and becoming a pastime. The possibility of streaming Internet content to TV, advances in video compression techniques and video streaming have turned this recent modality of watching movies easy and doable. Web portals as a worldwide mean of multimedia data access need to have their contents properly classified in order to meet users’ needs and expectations. The authors propose a set of semantic descriptors based on both user physiological signals, captured while watching videos, and on video low-level features extraction. These XML based descriptors contribute to the creation of automatic affective meta-information that will not only enhance a web-based video recommendation system based in emotional information, but also enhance search and retrieval of videos affective content from both users’ personal classifications and content classifications in the context of a web portal.

Apply of Cloud Mining and Taste Algorithm in Network Video Recommendation

Apply of Cloud Mining and Taste Algorithm in Network Video Recommendation

A Collaborative Filtering Recommendation Mechanism for Peer-to-Peer Video Sharing

Almost all collaborative filtering recommendation systems based on C/S mode have to face the problems of one-point-failure and unscalable. This study proposes a scalable collaborative filtering recommendation mechanism for video sharing in unstructured Peer-to-Peer (P2P) networks. The mechanism is named as CFRPV, which can recommend videos in distributed way. The CFRPV mechanism includes four parts: peer model definition, neighbor peer set construction, CF-based recommendation for videos and neighbor peer set update. In CFRPV, peer users rank all the videos that they had watched. Then a video can be represented as a point in video vector space and its rank is the value of this point. One peer’s preference also can be represented by a vector of the ranked videos in the video vector space. All the peers construct and dynamic reconstruct neighbor peer set in real time through calculating preference similarity between each other. From their neighbor peer sets, peers receive video recommendations that had been filtered. Finally, simulation results are discussed.

An experimental evaluation of ontology-based user profiles

In recent years, a number of research works have been carried out to improve the information retrieval process by exploiting external knowledge, e.g. by employing ontologies. Even though ontologies seem to be a promising technique to improve the retrieval process, hardly any study has been performed to evaluate the use of ontologies over a longer time period to model user interests. In this work we introduce an ontology based video recommender system that exploits implicit relevance feedback to capture users' evolving information needs. The system exploits a generic ontology to organise users' interests. We evaluate the recommendations by performing a user-centred multiple time-series study where participants were asked to include the system into their daily news gathering routine. The results of this study suggest that the system can be successfully employed to improve personal information seeking tasks in news domain.

Influences on TV Viewing and Online User-shared Video Use: Demographics, Generations, Contextual Age, Media Use, Motivations, and Audience Activity

People are increasingly viewing, providing, and recommending video content through the Internet. Applying the uses and gratifications framework, along with contextual age and generational theory, this study identifies and compares motivations for, and their influence on, traditional TV viewing and online user-shared video use among a U.S. sample of adult Internet users. Further, this study explores the form and role of audience activity through online user-shared video recommendations (type, channel, and social relation). Overall, the basic U&G motivations also apply to the new online media world, but differ in levels and influence.

Guest editorial: selected papers from ICIMCS 2011

International Conference on Internet Multimedia Computing and Services (ICIMCS) is an annual conference sponsored by ACM SIGMM China Chapter. The conference is especially interested in the latest technologies and applications that deal with the web-scale processing and management of heterogeneous data from the Internet for multimedia computing and service. ICIMCS 2011 held in Chengdu, China— the ancient hometown of lovely panda. The conference has attracted around 80 participants, including researchers from academia and industries across ten countries/regions, for sharing their recent works in the topics ranging from visual information analysis and mining, query processing and search, multimedia privacy and security. This special issue comprises the extended versions of five papers, including two best papers, and three papers from the regular and special sessions of ICIMCS 2011. These papers cover key issues in multimedia computing, including the leveraging of Internet resources for multi-modality fusion and visual classifier learning, preprocessing and indexing of million-scale Internet data, and sharing and streaming of Internet videos. Some of these techniques also demonstrate applications for emerging Internet services, such as video recommendation and product search system, by processing and modeling the heterogeneous forms of resources associated with multimedia data. The first two papers are about the web-scale processing of Internet data. The paper entitled ‘‘Video Recommendation over Multiple Information Sources’’, presented by Meng Wang and his colleagues from National University of Singapore, proposes a unified framework that explores heterogeneous information sources for video recommendation. The framework, based on multi-task SVM learning, aggregates multiple ranked lists generated from personal data, social network, and video metadata into an optimized list for recommendation. The framework is experimented on a large video dataset composed of 1-month social activities happened on Facebook and YouTube websites by 76 users. The second paper entitled ‘‘Multi-label Multiinstance Learning with Missing Object Tags’’, presented by Yi Shen and his colleagues from University of North Carolina at Charlotte, proposes a web-scale learning of object classifiers for free from a collection of user-tagged Flickr images as many as 10 millions. Particularly, the paper addresses three important issues toward fully automatic learning: scalable filtering of spam tags by distributed image clustering; joint modeling of loose tags and missing tags by multiple instance learning that is capable of performing tag prediction; structural learning that takes into account the object relationship to train discriminant classifiers. The next two papers address the search and mining of visual instances. The paper entitled ‘‘Combining Global and Local Matching of Multiple Features for Precise Item Image Retrieval’’, co-authored by Haojie Li and his colleagues from Dalian University of Technology and Nanjing C.-W. Ngo (&) City University of Hong Kong, Hong Kong, Hong Kong e-mail: cscwngo@cityu.edu.hk

Investigation of learners' perceptions for video summarization and recommendation

Recently, multimedia-based learning is widespread in educational settings. A number of studies investigate how to develop effective techniques to manage a huge volume of video sources, such as summarization and recommendation. However, few studies examine how these techniques affect learners' perceptions in multimedia learning systems. This article aims to examine learners' perceptions for summarization and recommendation, with an emphasis on the perspective of prior experience. In this study, we developed a multimedia content summarization and recommendation system, which can automatically extract summaries from raw video sources and recommend suitable video content to learners through emails. The results demonstrate that learners' prior experience and preferences for the presentation of document types affect their perceptions, including the enhancement of interests, the ease of information acquisition and the intention for the further use of the system. Finally, the findings are applied to develop a framework that can support for the design of multimedia learning systems.