Instagram 社群平台基於品牌形象之網紅推薦

Abstract

隨著社交媒體平台的日益普及，越來越多品牌僱用網紅提升產品的知名度和 品牌形象。多數品牌偏好與自身品牌形象相似的網紅合作，然而，就我們所知，現有網紅推薦模型都未曾考慮網紅風格與品牌形象的一致性。所以我們於本研究提出一個基於品牌形象的研究框架 BIARec，用以推薦網紅給品牌。此框架包含 三個模組，首先，我們開發一個特徵萃取模組，包含四個編碼模型:類型、喜好、 影響力和獨特性，藉此從品牌及網紅的社群媒體資料中萃取品牌和網紅特徵;接 著，我們採用對比學習模組學習更好的品牌和網紅特徵向量;然後，我們運用推 薦模組推薦網紅給品牌;最後，我們利用協作學習框架組合這三個模組，以增強 研究框架的泛化能力，並提高對抗噪聲數據的強度。實驗結果顯示，本研究提出的框架 BIARec 在曲線下面積、平均精確度均值、平均倒數排名、中位數排名、 精確率、召回率、F1 分數等方面優於最先進的模型，我們的推薦模型可以幫助 品牌找到風格一致的網紅，以推廣其產品和品牌形象。

With the surging popularity of social media platforms, an increasing number of brands employ influencers to boost their product visibility and brand image. Most brands tend to favor partnerships with the influencers whose styles are consistent with their brand image. However, to the best of our knowledge, there are no previous studies offering influencer recommendations to brands based on the alignment of influencer styles with their brand image. Therefore, in this study, we propose a brand-image-aware framework, called BIARec, to recommend influencers to brands. The proposed framework contains three modules. First, we develop a feature extraction module containing four encoders, namely type, favorability, strength, and uniqueness, to extract the brand and influencer feature vectors from the posts collected from Instagram. Second, we employ the contrastive learning module to better learn the representations of brands and influencers. Third, we apply a recommendation module to recommend influencers to brands. In addition, we utilize the collaborative learning framework to enhance model generalization and robustness. The experimental results show that our proposed framework outperforms the state-of-the-art models in terms of area under curve (AUC), mean average precision (mAP), mean reciprocal ranking (MRR), median rank (MedR), precision, recall, and F1-score. Our framework assists brands in identifying influencers whose styles align with their brand image, enabling effective promotion of their products and enhancing their brand image.