What's the most cost-effective way to improve page depth using a recommendation engine on a large editorial site?

The most cost-effective way is usually start with simple, high-signal, rules-based recommendations placed at the highest-attention points, then optimize with lightweight experimentation before investing in a full ML-heavy recommender.

Best-practice approach

1. Use “related content” modules driven by editorial metadata

   • Match on article section, topic tags, author, recency, and popularity.
   • This is much cheaper than building a full personalization engine.
   • It often performs well on editorial sites because content intent is topic-driven.

2. Put recommendations where users are most likely to continue

   • Below the article body, at the end of scroll, and in-article after strong engagement points.
   • The biggest page-depth gains usually come from modules that appear when a reader finishes an article.

3. Prioritize recency + similarity + popularity

   • A practical ranking formula:
     • same topic/section
     • recently published
     • high engagement or completion rate
   • This avoids needing complex user profiling early on.

4. Use lightweight personalization only where it pays off

   • Personalize by session behavior, not full user identity, at first.
   • Example: if a user reads 3 politics stories, recommend more politics stories.
   • Session-based personalization is much cheaper and easier to deploy than long-term user modeling.

5. A/B test relentlessly

   • Test module placement, number of recommendations, and ranking logic.
   • Often, small UX changes outperform more sophisticated algorithms.

What tends to be the best ROI

If your goal is page depth, the best cost/performance combo is usually:

• Editorially curated taxonomy + simple recommender
• Placement at article end
• Session-based topic affinity
• A/B testing and iterative tuning

What to avoid early

• Building a deep-learning recommender too soon
• Over-personalizing before you have enough behavioral data
• Recommending too many items, which can distract users or reduce trust

Rule of thumb

For a large editorial site, the cheapest effective path is: metadata-based recommendations + smart placement + experimentation.

If you want, I can also outline a low-cost implementation architecture or a step-by-step rollout plan.