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 textmeans 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 textscore and execution time — demonstrated that KD-tree achieved superior precision (0.75) and overall accuracy (0.65), whereas Formula: see textmeans obtained the highest recall (0.98). KD-tree produced outstanding results in food-related advertisements, while Formula: see textmeans 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 textmeans ensures broader user coverage, making each approach advantageous under specific advertising contexts.
Boughareb et al. (Thu,) studied this question.