Software development for contextual advertising of listings in the real estate domain

Advertising plays a crucial role in the success of a product, particularly in the real estate sector, where competition is fierce, and the properties' characteristics are complex. This article examines the advertising of real estate listings on a specialized aggregator website, which can provide additional context for the user's search, potentially enhancing the effectiveness of advertising campaigns. The paper discusses existing approaches and solutions for contextual advertising and sponsored search in real estate and the peculiarities of developing such solutions. It analyzes the main problems encountered in creating an algorithm for analyzing the context of advertising in real estate and proposes an alternative approach to implementing a contextual advertising algorithm, utilizing domain-specific expert knowledge. This approach to developing a contextual advertising algorithm may be more appropriate for organizations that lack the resources for developing and implementing machine learning-based solutions and associated data quality and volume management but possess expert knowledge in the field. To create such an algorithm, A/B testing is used to verify hypotheses related to the specificity of the listings and user behavior on the site, which allows not only to develop the algorithm but also to prove its effectiveness with real users. The article also notes the disadvantages of this approach, one of which is the long duration of the experiments. The paper presents the outcome of this approach in the form of an algorithm for real estate advertisement, which utilizes the characteristics of real estate objects, such as location, and the user's browsing history for remarketing. Using the UML language, component, and sequence diagrams of the example software for contextual advertising have been created.