NDLI: Latent Factor Representations for Cold-Start Video Recommendation

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Latent Factor Representations for Cold-Start Video Recommendation

Content Provider	ACM Digital Library
Author	Roy, Sujoy Guntuku, Sharath Chandra
Abstract	Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.
Starting Page	99
Ending Page	106
Page Count	8
File Format	PDF MP4
ISBN	9781450340359
DOI	10.1145/2959100.2959172
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Affective computing Personality Likes Emotion prediction Videos
Content Type	Audio Text
Resource Type	Article

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