In information retrieval, ranking search results is a critical task. Traditional methods often prioritize common features, which can lead to generic results. A novel approach to address this involves boosting rare features within the embedding space to improve re-ranking. This method emphasizes uniqueness and relevance, particularly in large datasets where common features may dominate. Link: https://dejanmarketing.com/introducing-dejan-re-ranking-algorithm/

Dejan's Re-Rank Algorithm

Article: https://dejanmarketing.com/query-found-to-be-the-biggest-predictor-of-ctr-delta/

In a deep CTR analysis of 30,000 query/URL pairs, we looked for relationships between query and basic snippet text features including title, description and URL. All combined feature attempts resulted in signal noise and weak correlations. In response, we trained four separate classifiers, each specialising in a single aspect of the analysed SERP snippet. Each classifier was used to process the entire dataset and the results were combined.

This lead to clearer correlations, better feature interpretability and the discovery of search query as the dominant factor in CTR delta prediction. This finding highlights the importance of query intent comprehension with significant implications on traffic modelling and projection tasks in search.

Dejan's Re-Rank Algorithm

DEJAN MARKETING

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