10.46243/jst.2023.v8.i12.pp168-184 registered

In the realm of digital advertising, particularly in the context of search engine advertising, businesses compete for visibility and user engagement. Search advertising recognition refers to the process of identifying relevant ads to display when a user performs a search query. The effectiveness of this recognition directly impacts the user experience and the revenue generated by advertisers and search engines. Traditional systems for search advertising recognition often relied heavily on keyword matching, bid prices, and ad quality scores. These systems used rule-based algorithms and heuristics to match user queries with relevant ads. While effective to some extent, they lacked the ability to understand the semantic context of the queries or the intent behind them. This limitation led to the development of more intelligent and adaptive systems. Thus, effective search advertising recognition is crucial for search engines like Google, Bing, or Yahoo, as well as for advertisers. Advertisers need their ads to be shown to the right audience, ensuring their investments translate into meaningful leads or sales. Users, on the other hand, rely on search engines to provide them with accurate and relevant results quickly. Therefore, this research aims to build a system with the goal is to identify the most relevant ads from a pool of available advertisements. The relevance of an ad is determined by various factors such as the semantic match between the query and the ad, historical user behavior, and the quality of the ad itself. The proposed model can accurately predict the user’s intent based on the query and select ads that are not only contextually relevant but also likely to result in user engagement