Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
Video relevance prediction is one of the most important tasks for online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized recommendations, which will help the user discover more content of interest. In most online service, the computation of video relevance table is based on users' implicit feedback, e.g. watch and search history. However, this kind of method performs poorly for "cold-start" problems - when a new video is added to the library, the recommendation system needs to bootstrap the video relevance score with very little user behavior known. One promising approach to solve it is analyzing video content itself, i.e. predicting video relevance by video frame, audio, subtitle and metadata. In this paper, we describe a challenge on Content-based Video Relevance Prediction (CBVRP) that is hosted by Hulu in the ACM Multimedia Conference 2018. In this challenge, Hulu drives the study on an open problem of exploiting content characteristics directly from original video for video relevance prediction. We provide massive video assets and ground truth relevance derived from our really system, to build up a common platform for algorithm development and performance evaluation.
Code (2)
Tasks
PredictionSimilar Papers 제목 키워드 기반
Feature Re-Learning with Data Augmentation for Video Relevance Prediction
Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image a…
Data AugmentationRetrievalTripletFusedLSTM: Fusing frame-level and video-level features for Content-based Video Relevance Prediction
This paper describes two of my best performing approaches on the Content-based Video Relevance Prediction challenge. In the FusedLSTM based approach, the inception-pool3 and the C3D-pool5 features are combined using an L…
TripletIceBreaker: Solving Cold Start Problem for Video Recommendation Engines
Internet has brought about a tremendous increase in content of all forms and, in that, video content constitutes the major backbone of the total content being published as well as watched. Thus it becomes imperative for …
Recommendation SystemsA Blast From the Past: Personalizing Predictions of Video-Induced Emotions using Personal Memories as Context
A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation. An important contextual influence shaping…
Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement
Video Corpus Moment Retrieval (VCMR) is a new video retrieval task aimed at retrieving a relevant moment from a large corpus of untrimmed videos using a text query. The relevance between the video and query is partial, m…
Moment RetrievalRetrievalVideo Corpus Moment RetrievalVideo Retrieval