Abstract
A novel method for interest level estimation based on tensor completion via feature integration for partially paired users' behavior and videos is presented in this paper. The proposed method defines a novel canonical correlation analysis (CCA) framework that is suitable for interest level estimation, which is a hybrid version of semi-supervised CCA (SemiCCA) and supervised locality preserving CCA (SLPCCA) called semi-supervised locality preserving CCA (S2LPCCA). For partially paired users' behavior and videos in actual shops and on the Internet, new integrated features that maximize the correlation between partially paired samples by the principal component analysis (PCA)-mixed CCA framework are calculated. Then videos that users have not watched can be used for the estimation of users' interest levels. Furthermore, local structures of partially paired samples in the same class are preserved for accurate estimation of interest levels. Tensor completion, which can be applied to three contexts, videos, users and “canonical features and interest levels,” is used for estimation of interest levels. Consequently, the proposed method realizes accurate estimation of users' interest levels based on S2LPCCA and the tensor completion from partially paired training features of users' behavior and videos. Experimental results obtained by applying the proposed method to actual data show the effectiveness of the proposed method.
Highlights
Interest level estimation is a technique that is useful in marketing for customer-oriented companies and users of their contents [1]–[7]
Results of the experiment that verify the effectiveness of using S2LPCCA and the tensor completion for interest level estimation are presented in V-B
We show the effectiveness of S2LPCCA and the tensor completion by using the graph that the mean absolute error (MAE)’ mean is distributed in each rate
Summary
Interest level estimation is a technique that is useful in marketing for customer-oriented companies and users of their contents [1]–[7]. Interest level estimation means predictions of users’ evaluations of various contents such as items in shops and videos on the Internet. The use of interest level estimation would enable companies to analyze their users’ shopping behavior and establish an effective strategy for selling their contents because they would know their users’ interests in their contents [1]. Liu et al focused on data for users’ behavior obtained from surveillance cameras for estimating users’ interests in actual shops [8]. They assumed that specific movements such as viewing and picking up items in the shop indicate a high level of interest, and such movements were extracted from the behavior data.
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