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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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Modeling Self-Representation Label Correlations for Textual Aspects and Emojis Recommendation.

Tianjun Wei, Tommy W S Chow, Jianghong Ma

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    Summary
    This summary is machine-generated.

    This study introduces a novel recommender system (CARAR) that enhances personalized suggestions by analyzing textual and emoji aspects in user reviews. The model effectively captures correlations between different review features for improved recommendations.

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    Area of Science:

    • Computer Science
    • Information Retrieval
    • Artificial Intelligence

    Background:

    • The proliferation of online platforms necessitates advanced recommender systems.
    • User reviews, encompassing text and emojis, offer rich data for aspect-based recommendations.
    • Existing systems struggle to effectively model personal preferences and semantic correlations within review aspects.

    Purpose of the Study:

    • To propose a novel correlation-aware review aspect recommender (CARAR) system.
    • To enhance personalized recommendations by integrating textual and emoji aspects.
    • To improve the handling of personal preferences and semantic correlations in recommendations.

    Main Methods:

    • Developed the CARAR model to construct self-representation correlations between textual and emoji aspects.
    • Utilized identified dependencies between textual aspects and emojis to learn user and item latent factors.
    • Incorporated cross-view correlation mapping and real-world environmental information to refine recommendations.

    Main Results:

    • The CARAR model demonstrated superior performance compared to six existing models across five datasets.
    • Experimental results validated the effectiveness of the proposed approach in review aspect recommendation.
    • The model successfully captured and leveraged cross-view correlations for enhanced recommendation quality.

    Conclusions:

    • The CARAR system offers a significant advancement in review aspect recommendation.
    • Integrating multi-modal review aspects (text and emoji) improves recommendation accuracy.
    • The model's ability to capture complex correlations leads to more personalized and effective suggestions.