Communications of the ACM
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Exploring Versus Exploiting when Learning User Models for Text Recommendation
User Modeling and User-Adapted Interaction
Video summarization based on user log enhanced link analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Video recommendations for the open video project
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
IEEE Transactions on Knowledge and Data Engineering
Learning Users' Interests by Quality Classification in Market-Based Recommender Systems
IEEE Transactions on Knowledge and Data Engineering
A Hybrid Movie Recommender System Based on Neural Networks
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Query enrichment for web-query classification
ACM Transactions on Information Systems (TOIS)
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Demographic prediction based on user's browsing behavior
Proceedings of the 16th international conference on World Wide Web
MultiTube--Where Web 2.0 and Multimedia Could Meet
IEEE MultiMedia
IEEE Transactions on Knowledge and Data Engineering
VideoReach: an online video recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
VideoSense: towards effective online video advertising
Proceedings of the 15th international conference on Multimedia
Online Index Recommendations for High-Dimensional Databases Using Query Workloads
IEEE Transactions on Knowledge and Data Engineering
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Video suggestion and discovery for youtube: taking random walks through the view graph
Proceedings of the 17th international conference on World Wide Web
Recommendation Method for Improving Customer Lifetime Value
IEEE Transactions on Knowledge and Data Engineering
A novel framework for efficient automated singer identification in large music databases
ACM Transactions on Information Systems (TOIS)
Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
CrowdReranking: exploring multiple search engines for visual search reranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Foundations and Trends in Information Retrieval
An attention-based decision fusion scheme for multimedia information retrieval
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Multi-Layer Multi-Instance Learning for Video Concept Detection
IEEE Transactions on Multimedia
Video Annotation Through Search and Graph Reinforcement Mining
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Optimization-based automated home video editing system
IEEE Transactions on Circuits and Systems for Video Technology
Modality Mixture Projections for Semantic Video Event Detection
IEEE Transactions on Circuits and Systems for Video Technology
Personalized video recommendation based on viewing history with the study on YouTube
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
SocialTransfer: cross-domain transfer learning from social streams for media applications
Proceedings of the 20th ACM international conference on Multimedia
Personalized video recommendation through tripartite graph propagation
Proceedings of the 20th ACM international conference on Multimedia
Fisher kernel based relevance feedback for multimodal video retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Multimedia encyclopedia construction by mining web knowledge
Signal Processing
Socially-aware video recommendation using users' profiles and crowdsourced annotations
Proceedings of the 2nd international workshop on Socially-aware multimedia
Semantic contextual advertising based on the open directory project
ACM Transactions on the Web (TWEB)
Hi-index | 0.00 |
With Internet delivery of video content surging to an unprecedented level, video recommendation, which suggests relevant videos to targeted users according to their historical and current viewings or preferences, has become one of most pervasive online video services. This article presents a novel contextual video recommendation system, called VideoReach, based on multimodal content relevance and user feedback. We consider an online video usually consists of different modalities (i.e., visual and audio track, as well as associated texts such as query, keywords, and surrounding text). Therefore, the recommended videos should be relevant to current viewing in terms of multimodal relevance. We also consider that different parts of videos are with different degrees of interest to a user, as well as different features and modalities have different contributions to the overall relevance. As a result, the recommended videos should also be relevant to current users in terms of user feedback (i.e., user click-through). We then design a unified framework for VideoReach which can seamlessly integrate both multimodal relevance and user feedback by relevance feedback and attention fusion. VideoReach represents one of the first attempts toward contextual recommendation driven by video content and user click-through, without assuming a sufficient collection of user profiles available. We conducted experiments over a large-scale real-world video data and reported the effectiveness of VideoReach.