From an article by Kevin Smokler at Paid Content.
The race to build this better discovery tool for readers is not only on—it is crowded. For the companies leaning hard on engineering, like Discoverreads and WhatShouldIReadNext, book data from the user (usually beginning with books they’ve already read/liked)) is crunched against preference patterns created by other users and run though a proprietary algorithm. Others services create a social experience around reading itself (BookGlutton with online annotation, Copia with discussion while reading or afterwards), and view discovery as a secondary benefit of participation. More general recommendation services like LivingSocial and GetGlue are hybrids—treating your new favorite book, video game or beer as a set of fertile data but only good for growing recommendations when planted alongside those of your friends and extended social network.
Book discovery is as necessary for the publishing business as it is difficult for the startups to solve. Readers seek out books for four primary reasons: familiarity with the author; interest in the subject; a recommendation from a trusted source; or hearing about it through the media. But readers state their book-reading preferences via dozens of smaller criteria—price and format, genre and setting, length and likeability of the protagonist or point of view.
The biggest Achilles’ heel in any book-finding service is getting good access to independent material, and not having that indy material be pushed aside and buried under major corporations’ promotional efforts. And as most big publishers seek out services and partnerships with such aggregate services, this is invariably what has happened. The result is that the content and recommendations become skewed towards the usual corporate content, because customers are more likely to buy the content they see.
I don’t know how an aggregate service is going to get around that problem, but I obviously hope someone figures out how to do it.
I follow a couple of book review lists, and they all have rules that they won’t review anything that hasn’t been professionally edited. And very few (any?) indy authors spring for external, professional editors. It’s hard to find review sites that review indy authors at all.
-becca
A great such tool already exists, and in fact has for over twelve years. I’ve covered it here before. Alexlit might be restricted to a slow, few-user interface right now, but at least it’s still running, and it’s still got a huge amount of user data built up.
If some tech or e-book site were smart, they’d contact Dave Howell and see about buying it up to give Howell more resources in return for incorporating Alexlit and its data into their site.
I’ve looked at a couple of algorithm-based recommendation sites, but, like Amazon’s recommendations, they’re very little use to me. They tend to say things like ‘You liked A Book by This Author. You might like These Other Books also by This Author’. Yes, I can figure that bit out on my own. I want to find other authors who write as well as This Author, possibly, but not necessarily, in the same genre. But, particularly with prolific authors, the recommendations get so crowded with Other Books by This Author (or worse yet, Other Editions Of The Same Book by This Author) that I can’t find anything else.
@Chris: That’s just the kind of thing that is bought up by a major publisher, or bought by a group that buys advertising/promos from major publishers, and thereby becomes skewed towards the majors.
That is, when someone actually discovers they’re there.
In my view personal taste can not and never will be predicted using any kind of “algorithm”. Not even close.
Personal taste is irrational, unpredictable and unique to each individual. Anyone investing money in a computer based, or tag based or meta data based system will lose their socks imho.
In my view the only basis for matching a person’s taste can come from personal recommendation and experience. Finding another person or small group of people who like the same style and titles is the only route to matching taste.
Therefore I believe that the methodology used by Copia et al, through social networks where individuals can match up with others of similar taste, is the one that will succeed.
As I have opined before, I also believe that there is a huge opportunity for prominent personalities with firmly honed personal reading preferences – or for ordinary individuals who read a lot AND who have identifiable coherent taste patterns, to develop their own personal recommendation sites for readers to follow. I can see a web circle of such personal recommendation sites developing. I can see readers following two or three of those people as a guide for finding new reads. I can see readers moving on from those to others as they exhaust or develop their tastes.
Howard: You should try out Alexlit. It uses collaborative filtering technology to match up people of similar tastes and recommend the books those others have rated highly, and I can tell you from experience, it really works.
@Howard: You’re describing the current critic system, which, again, tends to be skewed towards the big publishers who can afford to push their books at them…
Well, anyway.
Steven – I am sorry to disagree with you again 🙂 it is getting to be a habit … though of course these are only my personal opinions… 🙂
But is it really the current critic system ? I mentioned my belief in two potential ‘solutions’ and my disagreement with algorithm based strategies.
One was the completely ‘new’ Copia social reading solution, and the other is based on personal recommendations.
Social Reading sites as a strategy to find new reads are a new solution only launched recently and in their infancy, and I don’t believe there is any real presence of the kinds of I mentioned as my second solution. You mention critics … I am certainly not referring to any kind of critics sites.