隨著數位時代的演進、資訊技術的發展及人們生活方式的改變，許多線上平台已經陸續出現，以滿足現代的便利需求。這些平台包括串流服務，如Netflix和Disney+，以及電子商務平台，如Amazon、PC Home和Shopee。儘管有了這些進步，但使用者數量和產品種類呈指數級增長，導致了資訊量過大，迫使供應商和消費者將過多的時間和資源投入到決策中。為了解決此問題，推薦系統已經成為一個重要的解決方案，幫助使用者識別合適的產品，並協助平台實現目標市場行銷，進而節省成本。系統主要依靠消費者對於產品的評分及評論來產生預測。然而，產品和消費者數量的大幅增加帶來了挑戰，如數據的稀疏性。除此之外，納入當代消費者行為數據是一個必須解決的問題。
為了解決上述挑戰，本研究提出了一個集成神經網絡的應用方法來整合來自不同數據的偏好預測。粒子群最佳化演算法(Particle Swarm Optimization Algorithm; PSO) 被用來找出評分的最佳的相似性，緩解因數據稀少而產生的數據失真問題。而Bidirectional Encoder Representations from Transformers (BERT) 模型被用來從消費者評論中提取特徵，而InceptionV3則被用來從圖片數據中提取圖像特徵。藉由品項為基礎的協同過慮的實作，用來估計每筆數據的使用者偏好。
根據實驗結果，證實了所提出的整合數據的方法可以提高推薦系統的成效。藉由在六個Amazon數據集與基準方法相比，所提方法在其中五個方面表現優異。除此之外，該研究還發現「評論數據」和「圖片數據」的組合是最有效的數據組合。

As the digital age progresses, with information technology evolving and people's lifestyles changing, numerous online platforms have surfaced to meet modern convenience needs. These include streaming services such as Netflix and Disney+, and e-commerce platforms like Amazon, PC Home, and Shopee. Despite these advancements, the exponential growth in user base and product variety has led to an overwhelming amount of information, compelling both vendors and consumers to devote excessive time and resources to decision-making. In response, recommender systems have emerged as a crucial solution, aiding users in identifying suitable products and assisting platforms in achieving targeted marketing, thereby saving costs. These systems primarily rely on ratings or interactions between consumers and products to generate predictions. Nevertheless, the considerable expansion in the number of products and consumers has presented challenges, such as data sparsity. Additionally, the incorporation of contemporary consumer behavior data is an imperative issue to address.
To address the challenges outlined, this study proposes the application of an ensemble neural networks method to integrate preference predictions from diverse data sources. Particle Swarm Optimization is employed to identify optimal item similarity from ratings, mitigating issues of data distortion arising from data sparsity. The Bidirectional Encoder Representations from Transfomer model is utilized to extract features from consumer messages, while InceptionV3 is applied to extract image features from pictorial data. Furthermore, item-based collaborative filtering is implemented to estimate user preferences for each data source.
Based on the experimental findings, it is substantiated that the proposed method of integrating data sources can enhance the performance of the recommender system. In comparison to the benchmark methods applied across six Amazon datasets, the proposed approach outperforms in five of them. Moreover, the study also underscores that the combination of review data and picture data is the most effective data combination.

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