Added: Tarek Goble - Date: 21.12.2021 15:36 - Views: 47900 - Clicks: 6080
in. D ating is rough for the single person. Dating apps can be even rougher.
The algorithms dating apps use are largely kept private by the various companies that use them. Today, we will try to shed some light on these algorithms by building a dating algorithm using AI and Machine Learning. More specifically, we will be utilizing unsupervised machine learning in the form of clustering. Hopefully, we could improve the proc e ss of dating profile matching by pairing users together by using machine learning.
If dating companies such as Tinder or Hinge already take advantage of these techniques, then we will at least learn a little bit more about their profile matching process and some unsupervised machine learning concepts. However, if they do not use machine learning, then maybe we could surely How do dating site algorithms work the matchmaking process ourselves.
The idea behind the use of machine learning for dating apps and algorithms has been explored and detailed in the article below:. This article dealt with the application of AI and dating apps. It laid out the outline of the project, which we will be finalizing here How do dating site algorithms work this article.
The overall concept and application is simple. We will be using K-Means Clustering or Hierarchical Agglomerative Clustering to cluster the dating profiles with one another. By doing so, we hope to provide these hypothetical users with more matches like themselves instead of profiles unlike their own.
Now that we have an outline to begin creating this machine learning dating algorithm, we can begin coding it all out in Python! Since publicly available dating profiles are rare or impossible to come by, which is understandable due to security and privacy risks, we will have to resort to fake dating profiles to test out our machine learning algorithm. The process of gathering these fake dating profiles is outlined in the article below:.
Once we have our forged dating profiles, we can begin the practice of using Natural Language Processing NLP to explore and analyze our data, specifically the user bios. We have another article which details this entire procedure:.
With the data gathered and analyzed, we will be able to move on with the next exciting part of the project — Clustering! To begin, we must first import all the necessary libraries we will need in order for this clustering algorithm to run properly. We will also load in the Pandas DataFrame, which we created when we forged the fake dating profiles.
With our dataset good to go, we can begin the next step for our clustering algorithm. This will potentially decrease the time it takes to fit and transform our clustering algorithm to the dataset. Next, we will have to vectorize the bios we have from the fake profiles.
With vectorization we will implementing two different approaches to see if they have ificant effect on the clustering algorithm. We will be experimenting with both approaches to find the optimum vectorization method.
Here we have the option of either using CountVectorizer or TfidfVectorizer for vectorizing the dating profile bios. When the Bios have been vectorized and placed into their own DataFrame, we will concatenate them with the scaled dating to create a new DataFrame with all the features we need. Based on this final DF, we have more than features. This technique will reduce the dimensionality of our dataset but still retain much of the variability or valuable statistical information. What we are doing here is fitting and transforming our last DF, then plotting the variance and the of features.
This plot will visually tell us how many features for the variance. These features will now be used instead of the original DF to fit to our clustering algorithm. In order to cluster our profiles together, we must first find the optimum of clusters to create. The optimum of clusters will be determined based on specific evaluation metrics which will quantify the performance of the clustering algorithms. Since there is no definite set of clusters to create, we will be using a couple of different evaluation metrics to determine the How do dating site algorithms work of clusters.
These metrics each have How do dating site algorithms work own advantages and disadvantages. The choice to use either one is purely subjective and you are free to use another metric if you choose. Below, we will be running some code that will run our clustering algorithm with differing amounts of clusters. By running this code, we will be going through several steps:. Also, there is an option to run both types of clustering algorithms in the loop: Hierarchical Agglomerative Clustering and KMeans Clustering.
There is an option to uncomment out the desired clustering algorithm. To evaluate the clustering algorithms, we will create an evaluation function to run on our list of scores. With this function we can evaluate the list of scores acquired and plot out the values to determine the optimum of clusters. Based on both of these charts and evaluation metrics, the optimum of clusters seem to be For our final run of the algorithm, we will be using:.
With these parameters or functions, we will be clustering our dating profiles and asing each profile a to determine which cluster they belong to. With everything ready, we can finally discover the clustering asments for each dating profile. Once we have run the code, we can create a new column containing the cluster asments.
This new DataFrame now shows the asments for each dating profile. We have successfully clustered our dating profiles! We can now filter our selection in the DataFrame by selecting only specific Cluster s. By utilizing an unsupervised machine learning technique such as Hierarchical Agglomerative Clusteringwe were successfully able to cluster together over 5, different dating profiles.
Feel free to change and experiment with the code to see if you could potentially improve the overall result. Hopefully, by the end of this article, you were able to learn more about NLP and unsupervised machine learning. There are other potential improvements to be made to this project such as implementing a way to include new user input data to see who they might potentially match or cluster with.
Perhaps create a dashboard to fully realize this clustering algorithm as a prototype dating app. Link to the Web Application. Connect with me: linkedin. Your home for data science.
A Medium publication sharing concepts, ideas and codes. Get started. Open in app. in Get started. Get started Open in app. Marco Santos. The idea behind the use of machine learning for How do dating site algorithms work apps and algorithms has been explored and detailed in the article below: Can You Use Machine Learning to Find Love?
Getting the Dating Profile Data Since publicly available dating profiles are rare or impossible to come by, which is understandable due to security and privacy risks, we will have to resort to fake dating profiles to test out our machine learning algorithm. The process of gathering these fake dating profiles is outlined in the article below: I Generated Fake Dating Profiles for Data Science towardsdatascience. Preparing the Profile Data To begin, we must first import all the necessary libraries we will need in order for this clustering algorithm to run properly.
Evaluation Metrics for Clustering The optimum of clusters will be determined based on specific evaluation metrics which will quantify the performance of the clustering algorithms.
Finding the Right of Clusters Below, we will be running some code that How do dating site algorithms work run our clustering algorithm with differing amounts of clusters. Running the Final Clustering Algorithm With everything ready, we can finally discover the clustering asments for each dating profile. Closing Thoughts By utilizing an unsupervised machine learning technique such as Hierarchical Agglomerative Clusteringwe were successfully able to cluster together over 5, different dating profiles.
Check out the following article to see how we created a web application for this dating app: How I Used Streamlit to Build a Web Application towardsdatascience. More from Towards Data Science Follow. from Towards Data Science. More From Medium. Navoneel Chakrabarty in Towards Data Science. Handwritten Digits Recognition with Scikit-Learn. Bernice Lien in DataDrivenInvestor. Yoni Epstein. Rx finder Chatbot with Tensorflow. Karan Kashyap in Analytics Vidhya. Performing Sentiment Analysis on Movie Reviews. Bryan Tan in Towards Data Science.
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