Build a top-level sorting mechanism for the modules displayed on an e‑commerce site’s landing page. A module is a container that can hold many products—for example, a “Trending Now” carousel, a seasonal clearance banner, or a “Holiday Picks” gallery. The system must decide which of these blocks to present and in what order, driven by business goals like revenue, engagement, and customer lifecycle metrics. Your task is to describe the modeling philosophy, the serving architecture, and how you would measure success across the entire page, not just a single module.
Illustrative Scenario 1
Imagine a homepage with four slots. The candidate set includes a “Trending Electronics” carousel (refreshed every hour), a “Summer Sale” section (active for two weeks), a permanent “Deal of the Day” block, and a personalized “Recommended for You” shelf. A contract mandates that the “Summer Sale” must appear in the top two positions if it is active. Outline how you would rank these four modules for a returning customer, considering that the “Trending Electronics” content can change rapidly and the personalization module depends on user history.
Illustrative Scenario 2
A new “Back‑to‑School” module is launched with zero historical click data. The system needs to decide where to place it relative to well‑established modules like “Daily Picks” or “Clearance.” Explain how you would design a cold‑start exploration strategy that gathers signals without hurting overall page performance, and what metrics you would use to decide if the module deserves a permanent slot.
Design Considerations
While designing, explicitly address the following:
- What user actions define a successful homepage visit (clicks, add‑to‑cart, purchases, time on page, repeat visits)?
- Are there modules that are contractually guaranteed a fixed position, and how do they interact with the score‑based ranking?
- How fresh must time‑sensitive modules (trending, holiday) be, and what happens when the underlying data becomes stale?
- Can multiple modules display overlapping products, and is that acceptable for the business?
- What assumptions are you making about the number of slots, the candidate generation process, and the primary optimization objective? Distinguish clearly between ordering modules and ordering the items inside each module.
Key Elements to Cover in Your Solution
Your explanation should detail:
- How candidate modules become eligible (e.g., time windows, inventory checks, contractual rules).
- How you estimate the contextual value of a module (user segment, time of day, purchase history, device type).
- How slot position influences performance (higher positions get more attention; how do you model decay or click‑through rates per slot?).
- How to enforce diversity so that the final slate is not dominated by visually or semantically similar modules, and how you handle situations where several modules feature nearly identical product sets.
- An A/B testing framework that measures the impact on the complete homepage—including interaction effects across modules—rather than just the click‑through rate of a single block.
Follow‑Up Challenges
Explain how your design would handle:
- A first‑time visitor with no behavioral data.
- A promotional module that is entirely new and has no historical engagement (cold start).
- Preventing the page from showing two modules that are almost duplicates (e.g., “Under $25 Deals” and “Budget Picks” with 90% overlap).
- A situation where an experiment shows an increase in clicks but a simultaneous drop in purchases or customer satisfaction scores.
Assumed Constraints
- The recommendation system must serve a page layout in under 200 ms under normal load.
- The homepage typically shows between 3 and 5 module slots.
- The candidate pool consists of dozens of possible modules, each containing up to several hundred products, with new modules added seasonally.
- Business rules (e.g., contractual placements) may override the pure scoring logic for certain modules during specified time windows.
- Any solution must support full‑page A/B testing at a statistically rigorous level.