How do you use EDA to explore user preferences and behavior patterns for your recommender systems?

1 Data sources and formats

The first step in EDA is to collect and organize your data sources and formats. You need to have data that reflects the user interactions with your items, such as ratings, reviews, clicks, purchases, or views. You also need to have data that describes the characteristics of your items, such as genres, categories, tags, or attributes. Depending on your data sources and formats, you may need to perform some preprocessing, such as cleaning, merging, filtering, or transforming your data. You can use tools like pandas, numpy, or scikit-learn to handle your data manipulation tasks.

3 Data segmentation and clustering

The third step in Exploratory Data Analysis (EDA) is to segment and cluster your data. You need to group users and items based on their similarities or differences, which can be done by applying unsupervised learning techniques, such as k-means, hierarchical, or density-based clustering. Alternatively, supervised learning techniques like decision trees, logistic regression, or random forests can be used with tools like scikit-learn, scipy, pytorch, pandas, numpy. Through segmentation and clustering you can answer questions such as: how many clusters or segments are present in the data? What are their characteristics? How do they differ in ratings or interactions? How do they relate to item characteristics? How can you use them to personalize recommendations?

4 Data exploration and hypothesis testing

Exploring and testing your data is the fourth step in EDA. You can generate and validate hypotheses about your data, such as the causal relationships, the effects of interventions, or the differences between groups. To do this, you can use tools like scipy, statsmodels, or pytorch to apply statistical tests, such as t-tests, ANOVA, or chi-square tests. Additionally, you can use scikit-learn, pytorch, or tensorflow to apply machine learning models like linear regression, neural networks, or deep learning. Through data exploration and hypothesis testing, you can answer questions such as what factors affect ratings or interactions of users and items; how ratings or interactions change when manipulating item characteristics or recommendation strategies; how ratings or interactions vary across different groups of users or items; how well machine learning models fit data and predict ratings or interactions; and how to use machine learning models to optimize recommendations.

5 Data interpretation and communication

Interpreting and communicating data gathered through EDA is the final step. Summarize and explain your findings, insights, and recommendations based on the data. Utilize tools like matplotlib, seaborn, or plotly to create visuals that emphasize key points and reinforce your arguments. Additionally, pandas, numpy, or scikit-learn can be used to generate tables, metrics, or reports that illustrate results and performance. You can use data interpretation and communication to answer questions such as: What are the primary takeaways from your data analysis? What are the implications of your analysis? What actions can be taken based on your analysis? How will you present and share your analysis with stakeholders or an audience? How can feedback and evaluation be used to improve your data analysis and recommender systems?

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?