Investigating Centric and Hierarchical Approaches to Advertisement Recommendation

The growing need for personalised advertising has intensified interest in intelligent recommendation systems capable of adapting to user preferences. This paper introduces a collaborative advertisement recommender that integrates demographic, geographic and behavioural data to enhance ad relevance. Two clustering strategies are examined: a centric approach based on the [Formula: see text]means algorithm and a hierarchical approach employing the KD-tree algorithm. Experiments were conducted using data gathered from Hazmit, a purpose-built social platform for evaluating advertising recommendations. The comparative analysis — covering accuracy, precision, recall, [Formula: see text]score and execution time — demonstrated that KD-tree achieved superior precision (0.75) and overall accuracy (0.65), whereas [Formula: see text]means obtained the highest recall (0.98). KD-tree produced outstanding results in food-related advertisements, while [Formula: see text]means yielded stronger performance in technology and clothing categories. Both methods showed limited effectiveness for news advertisements, reflecting the unpredictability of user interests in that domain. With average runtimes below 1.2 s, both algorithms proved efficient for real-time deployment. Overall, the findings indicate that KD-tree offers more targeted and accurate recommendations, while [Formula: see text]means ensures broader user coverage, making each approach advantageous under specific advertising contexts.