The effect of different video summarization models on the quality of video recommendation based on low-level visual features

Published in Proceedings of the 15th ACM International Workshop on Content-Based Multimedia Indexing, 2017

Recommended citation: Yashar Deldjoo, Paolo Cremonesi, Markus Schedl, Massimo Quadrana Proceedings of the 15th ACM International Workshop on Content-Based Multimedia Indexing p. 20. (CBMI 2017).

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Abstract

Video summarization is a powerful tool for video understanding and browsing and is considered as an enabler for many video analysis tasks. While the effect of video summarization models has been largely studied in video retrieval and indexing applications over the last decade, its impact has not been well investigated in content-based video recommendation systems (RSs) based on low-level visual features, where the goal is to recommend items/videos to users based on visual content of videos. This work reveals specific problems related to video summarization and their impact on video recommendation. We present preliminary results of an analysis involving applying different video summarization models for the problem of video recommendation on a real-world RS dataset (MovieLens-10M) and show how temporal feature aggregation and video segmentation granularity can significantly influence/improve the quality of recommendation.