This paper presents cross-domain recommendation based on multilayer graph analysis using subgraph representation. The proposed method constructs two graphs in source and target domains utilizing user-item embedding and trains link relationships between the users’ embedding features on each above graph via graph convolutional networks considering subgraph representation. Thus, the proposed method can obtain features with high representation ability, and this is the main contribution of this paper. Then the proposed method can estimate the user’s embedding features in the target domain from those in the source domain and recommend items to users by using the estimated features. Experiments on real-world e-commerce datasets verify the effectiveness of the proposed method.
KEYWORDS: Visualization, Feature extraction, Visual analytics, Matrices, Canonical correlation analysis, Information visualization, Principal component analysis, Information science, Information technology, Video
This paper presents gaze-based visual feature extraction via Discriminative Locality Preserving Canonical Correlation Analysis (DLPCCA) for visual sentiment estimation. The proposed method calculates novel visual features reflecting users’ visual sentiment by applying DLPCCA to gaze and original visual features. Consequently, accurate visual sentiment estimation becomes feasible by utilizing the novel visual features derived by the proposed method.
This paper presents a classification method of tourism categories based on heterogeneous features considering existence of reliable results. The proposed method performs estimation of existence of reliable results based on one-versus-one scheme from three kinds of classification results obtained from tourism images, geotags and textual tags, separately. Then if the reliable result is included in the above results, this result is regarded as a final result. Otherwise, the final result is obtained by the multiple annotator logistic regression. The proposed method realizes accurate classification by estimating the existence of reliable results from more than two kinds of results.
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