Recommendation algorithms

Popular recommendation algorithms serve up those products that are, you guessed it, most popular. These products are the best sellers across a brand’s entire inventory and don’t take individual customers’ personal tastes and data into consideration here, nor where they may be along the customer journey.

good for new users

Popular/Best sellers

If you release a new product that suddenly surges in sales, the trending recommendation algorithm will prioritize it over the consistent best sellers.

good for holiday sales

Contexual (Collaberative filtering - Amazon)

Contextual recommendations are part of a category called

collaborative filtering⁠

, otherwise known as “Those who bought this also bought that.” To work, these recommendation algorithms need a single data set: the URL a customer is viewing. From there, they leverage the larger site history to make recommendations.

good for all pdp pages

Browse history

Recommendations based on browsing history also use collaborative filtering to suggest items that have compelled customers with similar histories to buy. Great for product discovery, these recommendations can be used with customers who have generated as few as two page views. However, extensive browsing histories can make them more effective.

One way to use them: Knowing what a customer viewed is all you need to deploy a strong

browse abandonment email⁠

How to implement item-to-item collab filtering

Collect Data: Collect data on user behaviors such as the items they have viewed, purchased, added to their cart, and rated.

Prepare Data: Preprocess the data by cleaning and normalizing it, and then transform it into the format required by the item-based collaborative filtering algorithm. Typically, this involves creating a user-item matrix, where each row represents a user, each column represents an item, and each cell represents the user's interaction with the item (such as a rating or purchase).

Calculate Similarities: Calculate the similarities between the items based on the user-item matrix. One way to do this is to use a similarity metric such as cosine similarity, which measures the cosine of the angle between two vectors.

Generate Recommendations: For a given user, identify the items they have interacted with, and then use the similarities between those items and other items to generate a list of recommended items that the user has not yet interacted with.

Integrate the Algorithm: Create an API that takes in user data and provides personalized recommendations based on the item-based collaborative filtering algorithm's output. The API should include endpoints for adding user data, generating recommendations, and retrieving recommendation results.

Test and Evaluate: Test and evaluate the performance of the item-based collaborative filtering algorithm by measuring the accuracy of the recommendations and the user engagement on the app. This can involve splitting the user-item matrix into training and testing sets, and using metrics such as precision and recall to evaluate the algorithm's performance.

Refine and Improve: Based on the performance evaluation, refine and improve the algorithm by tuning the hyperparameters or using a different algorithm.

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