Alsobought

Alsobought is a term used in retail analytics and recommender systems to describe signals that indicate products commonly purchased together by customers. It refers to the practice of suggesting items to buyers based on co-purchase patterns, typically presented as “customers who bought this item also bought.” The concept is closely associated with online retailers and is a core component of many product recommendation engines.

In practice, alsobought signals are derived from transaction data and analyzed with methods such as market basket analysis, association rule mining (for example, Apriori or FP-growth algorithms), and various forms of collaborative filtering. Key metrics include support (how often items occur together), confidence (the probability of purchasing a second item given the first), and lift (the strength of the association beyond random chance). These signals can be modeled as co-purchase networks or item-to-item recommendations that update in real time or in batch intervals.

Applications include on-page recommendations, shopping-cart upsells, personalized emails, and automated product bundling. They aid in cross-selling, inventory planning, and measuring the impact of recommendations on sales and engagement. However, alsobought systems face limitations such as popularity bias toward well-known items, cold-start challenges for new products, data sparsity, and privacy considerations. Ongoing refinements aim to incorporate context, recency, and user behavior to improve relevance.

See also: association rule learning, market basket analysis, collaborative filtering, recommendation systems, cross-selling.