Introduction

Scenario Design is a framework that shifts attention to serving human goals. It asks three central questions: Who are the users? What are their goals? And how can the system help them achieve those goals? For this assignment, I will be applying this lens to Goodreads.

Goodreads is a popular book-tracking and recommendation platform (owned by Amazon). It helps shapes what readers (like myself) discover and discuss. However, despite its ealth fo knowledge on book ratings, reviews, reading lists, etc. many avid readers—including myself—find its recommendations…lacking. Personally, I usually find it influenced by mainstream popularity strongly, rather than influenced by what I personally like to read and have told it I already read. Through scenario design, this assignment explores how Goodreads’ recommender system serves (and fails) both its readers and the platform itself, and suggests how it could evolve into a more meaningful tool.

Scenario Design Analysis — Goodreads

1. Who are the target users?

Goodreads has several overlapping user groups:

  • Avid Readers who log every book they finish and seek personalized recommendations.
  • Casual Readers who read occasionally and want light, mainstream suggestions without too much effort.
  • Explorers and Collectors who use Goodreads to organize their “to-read” list, track progress on that list, and discover lesser-known titles.
  • Reviewers and Social Readers who enjoy sharing opinions, following friends, and looking at community reviews.

2. What are their key goals?

From the user perspective, Goodreads helps satisfy a mix of goals.

Discovery Goals

  • Find books they’ll love, not just bestsellers.
  • Identify new authors or series similar to favorites.

Tracking & Organization Goals

  • Maintain a record of what they’ve read, are reading, and want to read.
  • Categorize books by theme, series, or personal shelves.

Community & Validation Goals

  • See what trusted friends or reviewers are reading.
  • Contribute reviews and participate in discussion.

Emotional Goals

  • Experience the joy of discovering the right book again.
  • Experience nostalgia for favorite genres, characters, series etc.

3. How can the system help them accomplish those goals?

Goodreads’ recommender system helps users through a combination of collaborative filtering, content-based similarity, and social graph signals:

  • Collaborative Filtering: Recommends books that other users with similar rating histories have enjoyed.
  • Content-Based Filtering: Uses metadata (author, genre, tags, keywords) to surface similar works.
  • Social Recommendations: Highlights books liked by friends, authors followed, or members of groups the user has joined.

While these techniques support users’ goals, the experience often feels popularity-biased, and like the algorithm isn’t really finding those “that’s for me” stories. The system emphasizes well-known titles and seems to neglect lesser-known or niche works a bit. The “Because you liked…” section doesn’t usually feel super close to what I actually did like.

To better serve the user, Goodreads should evolve from it’s sort of content catalog platform, into more of a reading companion. The better is understands why a reader liked a particular book (style, tone, historical period, pacing, genre, etc.), the more value it can add.

Reverse-Engineering Goodreads’ Recommender

Although Goodreads’ algorithms are proprietary, my observations (and other observations loosely documented on the internet) lead me to these pieces that feed into the current recommender:

  1. Data Inputs: Ratings (1–5 stars), shelves (“read,” “to-read,” custom lists), and genre tags.
  2. Behavioral Signals: Clicks on author pages, time spent reading reviews, additions to “want to read.”
  3. Collaborative Graph: Co-rating similarity among users.
  4. Surface Design: Recommendations appear on homepages, “Readers Also Enjoyed” carousels, and emails.

From what I have seen, my guess is that Goodreads weights explicit ratings more heavily than implicit engagement, making it slow to adapt to changing tastes. A reader who once loved fantasy but now focuses on biographies will continue to see fantasy-heavy feeds for a long time.

Goodreads’ Organizational Goals

From the company’s perspective, Goodreads supports Amazon’s broader ecosystem:

While these align partially with user satisfaction, optimizing for sales and click-throughs can really reduce new or personalized discovery. The reinforcing of mainstream trends can feel pretty limiting for devoted readers.

Recommendations for Improvement

  1. Deep Preference Profiling Gather richer preference dimensions (tone, pacing, setting, emotional intensity) through quizzes or short “reading mood” prompts.

  2. Transparent Explanations Add brief notes like “Because you enjoyed WWII historical fiction” to clarify why each book appears. Transparently telling the user why they are getting a recommendation improves trust and engagement.

  3. User Feedback Controls Enable thumbs-up/down or “Not Interested” options to refine results. Active feedback helps the system learn user intent faster.

  4. Mood and Context Modes Introduce browsing options such as “Cozy Reads,” or “Deep Thinking,” to tailor recommendations to the reader’s different mindsets

  5. Community-Driven Discovery Weigh books popular in friends’ groups more heavily than global popularity.

  6. Hidden Gem Surfacing Dedicate a portion of recommendation slots to “Hidden Gems” — lesser-known books with high satisfaction scores.

  7. Adaptive Shelf Learning Analyze custom shelf names (like “easy read fantasy” or “powerful women biographies”) to form more nuanced personalization.

Conclusion

Goodreads has the data, scale, and community to be an enormous and insightful reading companion, but its recommender is currently more like a catalog than a guide. By focusing on user goals of more personalized discovery, emotional resonance, and authentic connection, the platform could evolve into a go-to discovery and engagement tool for avid readers.

References