Improving Ranking with a Custom TF-IDF Algorithm in Python for SEO
I'm writing unit tests and Currently developing a machine learning model to optimize SEO rankings for a set of client websites. One aspect of this project involves implementing a custom TF-IDF algorithm to better analyze keyword importance across our document corpus. While using the `scikit-learn` library for basic TF-IDF calculations, I've noticed it does not capture the nuances of some of our long-tail keywords. Initially, I used the following code snippet to generate the TF-IDF matrix: ```python from sklearn.feature_extraction.text import TfidfVectorizer corpus = [ 'SEO optimization techniques for better ranking', 'Understanding SEO and its importance', 'Keyword analysis as a part of SEO strategy' ] vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(corpus) print(vectorizer.get_feature_names_out()) print(X.toarray()) ``` However, when analyzing the output, it became evident that the model was not prioritizing certain key phrases that are critical for our target audience. Next, I experimented with adjusting parameters like `min_df` and `max_df`, but the changes were marginal. To gain more control, I’m considering implementing a weighted version of TF-IDF, which factors in external metrics like click-through rates (CTR) and average time on page. I've looked into integrating additional metrics using a custom function within the `TfidfVectorizer`. Here’s a preliminary draft of what that might look like: ```python def custom_tfidf_weighting(tfidf_matrix, external_metrics): # Logic to weight TF-IDF scores based on external metrics weighted_matrix = tfidf_matrix.copy() for i in range(weighted_matrix.shape[0]): for j in range(weighted_matrix.shape[1]): weighted_matrix[i, j] *= external_metrics[i] # Hypothetical external metric return weighted_matrix ``` Although this approach seems promising, I’m unsure how to effectively combine the TF-IDF scores with these external metrics. Also, any insight into handling cases where external metrics may not be available for all documents would be beneficial. The goal is to enhance the relevancy of our keyword analysis without sacrificing the model's performance. Any suggestions on best practices or alternative algorithms that can handle this scenario would be greatly appreciated. This is part of a larger application I'm building. Any ideas how to fix this? I'm working in a Linux environment.