ایجاد مدل ماشین لرنینگ با استفاده از الگوریتم‌های رتبه‌بندی

این پروژه به صورت اختصاصی برای آقای هادی فتحی پور ایجاد شده است. لطفا از ارسال پیشنهاد خودداری فرمایید. .برای دیتاستی با ده هزار وبسایت و 38 پارامتر برای هر یک از آنها، به سراغ بخش ماشین لرنینگ پروژه میرویم که به منظور حفظ شفافیت به صورت انگلیسی ارائه شده است: 1. Training and fine-tuning models on training and validation data: As this is a ranking problem, we need to use Learning to Rank (LTR) algorithms, notably XGBoost, LightGBM and RankNet that use supervised machine learning to solve ranking algorithms [21]. We will divide our data into two segments: training and test data. We will keep the unseen test data for final evaluation, and we use k-fold cross validation to train our model [22]. In general, with cross validation, we divide our data into k folds, but for LTR algorithms, cross validation is a bit different since we need to divide our keywords into train and validation parts. Also, for the test data it is the same, and we need to keep part of our keywords and related webpages to those keywords as our test set. To evaluate our models, we use a metric called Normalized Discounted Cumulative Gain (NDCG) that takes ranking into account [26]. We can use NDCG@K with K as the number of results which in our case is 50 because we consider the first 5 pages of Google search results. For example, we know the true Google ranking for the first 50 webpages related to a specific keyword. We put these webpages in a table and change the ranking. Then, we ask the model to rank this list, and we compare this ranking that is generated by our model to the true Google ranking for that keyword. Our models involve parameters that can be tuned to improve the model results. We will use randomized search cross validation (CV) that is a famous hyper parameter tuning to fine tune our model and choose a combination of parameters that results in a better NDCG score. 2. Testing the models on test data: We will check the performance of the trained models against unseen test data based on NDCG@50 evaluation metric [22]. 3. Selecting the best option: We will compare the models and find the one that has the best result and can better predict on our test data. 4. Framework Construction After we selected the model that can help us know which factors are more important in SEO, we should construct a framework. In this framework, we explain the prediction of ranks with the selected model by creating a feature importance table to show and rank the importance of each factor. In more details, more important features have a higher impact on the webpage’s rank, and online marketers can use this table to prioritize their plans. Also, we visualize our explanation using some techniques like SHAP (SHapley Additive exPlanations) values. SHAP values help us better understand complex machine learning models [27]. The feature importance shows us which features have a higher impact on the rank of a webpage in search engine results, and SHAP values define this impact is positive or negative. Also, we try to provide actionable insights for online marketers and content creators and explain the results in a clear and simple way and help them how to use the model for different industries and keywords. Based on these results, online marketers know where to focus and which features are more important and should be improved as a higher priority when they want their webpages to appear on the first Google search results. برای فاز اول پروژه که ایجاد مدل است، accuracy یا دقت 60% و در فاز دوم که بهینه‌سازی است، accuracy یا دقت 85 درصد مد نظر است.