Session recommendations

Recommendations are the working engine behind a personalized agenda: the agenda is the plan, recommendations are how it gets populated and refreshed. The quality ceiling is set by data. Interest tags from registration give a starting point; behavior — sessions favorited, searches run, talks actually attended — sharpens it as the event progresses. This creates a cold-start problem: on day one, before any behavior exists, recommendations lean on thin profile data and are at their weakest exactly when attendees are forming their opinion of the feature. Organizers can soften this by asking two or three sharp interest questions during registration and by seeding recommendations from what similar attendees chose at the last edition. Presentation matters as much as the algorithm. A short reason attached to each suggestion — "because you favorited the AI in logistics track" — makes recommendations feel considered instead of random, and measurably improves uptake. The honest nuance is the similarity trap: engines tuned purely on "more like what you clicked" produce a narrow bubble, and part of why people attend events is discovering things they didn't know to search for. Deliberately mixing in one or two adjacent-topic suggestions keeps serendipity alive, which is harder to justify in a metrics review but closer to what attendees actually value.