Estimation of Interest Levels From Behavior Features via Tensor Completion Including Adaptive Similar User Selection

Abstract

A method for estimating interest levels from behavior features via tensor completion including adaptive similar user selection is presented in this paper. The proposed method focuses on a tensor that is suitable for data containing multiple contexts and constructs a third-order tensor in which three modes are "products", "users" and "user behaviors and interest levels" for these products. By complementing this tensor, unknown interest level estimation of a product for a target user becomes feasible. For further improving the estimation performance, the proposed method adaptively selects similar users for the target user by focusing on converged estimation errors between estimated interest levels and known interest levels in the tensor completion. Furthermore, the proposed method can adaptively estimate the unknown interest from the similar users. This is the main contribution of this paper. Therefore, the influence of users having different interests is reduced, and accurate interest level estimation can be realized. In order to verify the effectiveness of the proposed method, we show experimental results obtained by estimating interest levels of users holding books. INDEX TERMS Interest level estimation, behavior feature, similar user selection, tensor completion, missing value estimation.

Feature Integration via Semi-Supervised Ordinally Multi-Modal Gaussian Process Latent Variable Model

“…By inputting each frame of these videos, we calculated 2, 048-dimensional outputs from the middle layer of Inception-v3 [29]. Then we calculated mean vectors of the output vectors as content feature vectors y c n in the same manner as [17]. In this experiment, 1) subjects watched one video for 30 seconds and 2) evaluated the video in four ordinal classes 4 and provided a label.…”

“…Furthermore, users' interest levels are not as simple as a binary value, and they have different levels depending on its ordinal degree. That is, the levels are expressed as ordered values [17,18]. Therefore, for efficient feature integration, consideration of unlabeled samples and the degrees between labels is needed.…”

Trial Analysis of Brain Activity Information for the Presymptomatic Disease Detection of Rheumatoid Arthritis

“…In this study, we conducted a trial analysis for the early detection of presymptomatic RA using missing value completion techniques for brain activity information and correlation analysis capable of assessing multiple aspects of brain activity. Specifically, we used a matrix factorization-based approach to address missing brain activity information, confirming its efficacy in complementing missing biological data, including behavior [ 11 ] and brain [ 12 ] data. In order to identify crucial brain regions for classifying F759 and wild-type mice, we employed canonical correlation analysis, a method widely used for analyzing various brain activity information [ 13 , 14 , 15 , 16 , 17 ].…”

Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

“…In studies using wearable sensors, healthcare monitoring systems using vital data, such as blood pressure, heart rate, weight, and blood glucose, have been proposed [ 17 , 18 ]. Additionally, several studies estimated the interests of content involving human behavior [ 19 , 20 ] and personalized saliency (and its prediction) using gaze data [ 21 , 22 , 23 ]. Furthermore, the analysis of behavior, gaze data, and brain activity contribute to the solutions to several tasks, such as brain decoding [ 24 , 25 , 26 , 27 ] and certain applications [ 28 , 29 , 30 , 31 , 32 ].…”