I have an online tutoring marketplace similar in functionality to Clarity.fm. Our tutors with the highest ratings and most reviews for each subject tend to get overloaded with messages because they come up first in the search results. I'd like to reward these tutors for getting great ratings and reviews while still spreading the wealth to our newer tutors who have no ratings or reviews. Are there any best practices, resources, frameworks for developing an ideal a) "Best Match" algorithm for students searching for a tutor and b) Automatic Matching for students that want us to choose their tutor? Some inputs we have at our disposal: -Course/Subject match proximity -School -Graduation Year -Major -Timezone -Availability -# of Ratings -# of Sessions -Average Rating -Sign Up Date -Responsiveness -# of messages this week / month
I've worked on a few different matching / recommendation algorithms, and to some extent, we did exactly this at Skillshare.
For both "best match" during a search, as well as the default or auto-match, it's all about relevance.. there shouldn't really be a different goal for each.
If I'm searching for a specific course or subject, you should filter by that category.. then use things in the student's profile (e.g. major, year / age-range, etc) to determine best relevancy on the tutor side.
Without specifics, then you could at least use more general inputs, like location (in-person tutoring?) and time zone, to determine relevancy.
Beyond these, I would offer a filter / sort based on "new" or "featured" (curated) vs. "popular" or "highest rated" .. but use your internal algorithm (and UX design) to tweak whatever you want.
Happy to help and discuss in more detail based on the specifics of your business.
Answered 9 years ago
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