There is no one-size-fits-all recommendation.

AI recommendation algorithms are now so natural. "Recommendations for you" are offered first in online bookstores, e-book platforms, and content services. However, this familiar technology often stumbles when faced with senior readers. The problem isn't the accuracy of recommendations. It lies in the very premise. For seniors, reading isn't about "consuming tastes," but "using time," and not "searching for information," but "recalling and connecting with life."

Market Needs: Senior Reading Has Different Purposes

Typical recommendation algorithms operate based on clicks, purchases, ratings, and dwell time. This data is optimized to predict what readers will consume next. However, senior readers prioritize density over speed, meaning over new releases, and context over trends. Rereading books, authors they already know, and topics that connect with past experiences are crucial. In other words, it's closer to "confirmation" than "discovery." This misaligns the goals for which algorithms are designed with senior readers in mind.

Failure Point 1: Lots of Data, No Context

The reading history of senior readers is difficult to capture through data. Offline reading accounts for a large portion of reading, and there's often a discrepancy between purchases and actual reading. The most crucial question is "why do people read these books?" But algorithms don't understand "why." A business book read after retirement might be the same genre, but it carries a completely different meaning than one read in one's youth. Algorithms focus on genre, while seniors focus on the context of life.

Failure Point 2: Recommendations Reduce Choices, but Seniors Savor Choices

Recommendation systems quickly narrow down choices, a benefit for efficiency-conscious users. However, for seniors, reading is a crucial activity in itself: the decision-making process. The time spent selecting a book at a bookstore, examining the cover, flipping through the table of contents, and deliberating is part of the reading experience. Automated recommendations eliminate this time. While they offer convenience, they reduce satisfaction.

Failure Point 3: Personalization Becomes "Pastification"

Senior recommendation algorithms often operate on historical bias rather than personalization. "I've read this book before, so here's a recommendation for similar books." This is safe, but boring. Senior readers don't completely reject new things; they simply want to be told how they connect to their lives. Algorithms suggest similarities, but don't explain connections.

Technical Challenges: Interface Problems, Not Algorithms

Trying to solve this problem simply through advanced algorithms will fail. What's needed is a question that precedes the recommendation logic. Questions like "What phase of life are you in right now?" and "What are you thinking about these days?" are far more important than click data. Recommendations tailored to seniors are closer to "conversational curation" than predictive models. The role of technology is not to provide answers, but to ask questions that facilitate recall.

Iropke's Perspective: Designing Recommendations as a "Process," Not an "Outcome"

Iropke doesn't view recommendation as a standalone feature in senior services. Recommendations aren't a single screen, but a journey. Algorithms don't take the lead; the user's narrative and memories come first. Data doesn't replace choices; it gently assists them. What seniors need isn't "books you'll like," but "a way to respect their time."

In conclusion: Technology may be accurate, but it may not be enough.

The reason recommendation algorithms fail for seniors isn't because of a lack of technology. It's because they're too fast and too definitive. Reading for seniors isn't about consumption, it's about conversation. Only when technology slows down this conversation and refines the questions do recommendations truly become meaningful. While technological advancement has always accelerated, the key to designing a senior experience lies in slowing it down.