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Online advertising and recommendation systems have become vital components in the digital economy, yet they face several challenges in their design, implementation, and effectiveness. One of the primary challenges is dealing with massive volumes of heterogeneous data generated by users across platforms. The data comes from clicks, search histories, social interactions, and purchase behavior, which makes it complex to integrate and analyze. Privacy concerns further complicate the situation as collecting and using personal data for targeted advertising must comply with strict data protection regulations. Another issue is the problem of sparsity, where users interact with only a small fraction of the available items, making it difficult to generate accurate recommendations. Additionally, recommendation systems often face the cold start problem, where new users or new items lack sufficient interaction history to ___ ________ _______ __________ ___ ____ _______ ______ ____.
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