Product Recommendation

Recommendation algorithms are broadly classified into three types. "Collaborative filtering" leverages behavioral data from users with similar purchase patterns. Amazon's "Customers with similar buying habits also purchased this" is a prime example. It requires large volumes of user data but has the advantage of working without product attribute information, making it highly versatile.

"Content-based filtering" recommends products based on the similarity of product attributes (category, brand, price range, material, etc.). It suggests products with attributes similar to those the user has purchased in the past, enabling reasonably accurate recommendations even for new users. "Hybrid" approaches combine both methods and are used by Netflix and Spotify. In recent years, deep learning-based recommendations have also become widespread, integrating diverse data types including text, images, and behavioral logs to improve recommendation accuracy.

Recommendations are a critical feature that directly impacts EC sales. At Amazon, approximately 35% of sales reportedly come through recommendations, and at Netflix, about 80% of viewed content is selected from recommendations. For businesses, implementing effective recommendations directly contributes to higher average order values, increased purchase frequency, and reduced churn rates. They are embedded at each stage of the purchase process - "Customers who viewed this also viewed" on product pages, "Frequently bought together" on cart pages, and "Recommended for you" in emails.

As a consumer, it is important to recognize that recommendations serve as both a "helpful assistant" and a "mechanism to encourage purchases." Since algorithms make suggestions based on past behavior, browsing a particular category once can trigger repeated display of similar products - a "filter bubble" effect. It is essential to calmly assess whether a product is truly needed and consciously avoid unnecessary purchases driven by recommendations. Resetting browsing history or using incognito mode can also help reduce recommendation bias.