Ask anyone for book recommendations or give one

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I'm a regular visitor of books-related subs on Reddit and my fomo regarding an excellent book goes out of control whenever I see related threads on HN. I've noticed that a lot of people want book recommendations but they don't want the "best" books in a field. They are looking for something that falls in their area of reading interest and something similar to what they've read earlier and liked. --> In the absence of any context of what a person has been reading, it becomes difficult to recommend a book to him. --> On the other hand, if I know someone likes reading biographies, my first reaction on discovering a great biography is to either message them about the book or try to "sell them the idea" of reading this book. Both the approaches are easily forgotten. So to make asking for book suggestions easy, I've added this feature on ShelfJoy, called Recommend a book. Now, you can simply share your ShelfJoy profile with your friends/with people whose recommendations you trust and ask them to recommend a book to you. Example: I am on a reading challenge in 2017 where I am reading a business biography every month. By sharing my profile here: http://shelfjoy.com/sia_steel/recommendations I can simply ask for recommendations and friends can browse through my reading list, books that I've liked before and recommend me books they think I'll like. My entire recommendations stay at the same place and I can simply shift that book to the books that I've read once I finish it. + I'm looking forward for some feedback on this feature and whether you find it useful.

Visit product View on Hacker NewsJun 4, 2017

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Product HuntOn track for Day 1 leaderboard · Strong signals: context · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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