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Online advertising and recommendation systems have become integral components of modern digital platforms, playing a pivotal role in shaping user experiences and driving business revenues. However, despite their widespread adoption, these systems face a series of complex challenges, particularly when examined through the lens of data science and big data. One of the primary challenges in online advertising is targeting the right audience accurately while respecting privacy concerns. Digital platforms collect vast amounts of user data, including browsing history, demographics, location, and behavioral patterns. While this data provides opportunities to deliver personalized ads, it also raises significant ethical and technical concerns regarding user consent and data security. Additionally, the massive scale of online interactions generates high-velocity and high-volume data streams that must be processed in real-time to ensure that ads are relevant and timely. Handling such large-scale data requires robust distributed computing frameworks, often leveraging big data technologies such as Hadoop, Spark, or Kafka to manage storage and computation efficiently. Another critical challenge is click fraud and ad view manipulation. Fraudulent activities, where automated bots simulate clicks or views, can severely impact advertisers’ return on investment. Detecting and mitigating these fraudulent behaviors demands advanced anomaly detection algorithms and real-time monitoring, which can be computationally intensive due to the continuous influx of data. Furthermore, measuring the effectiveness of online ads is inherently complex. Traditional metrics such as click-through _________ _______ _______ _________ ___ ___.
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