PlaystarSCimagexProof._DSC3121Content filters are evolving all the time, and they’re changing how we discover the entertainment we love. Pretty soon we won’t just be searching for simple things like “Romance Novels” or “YA Fiction”—we’ll be able to spell out our interests into extremely focused queries like “romantic comedy book featuring two dog-loving and socially awkward lawyers with wildly different political views” or “dystopian novel featuring young green-eyed girl who discovers she’s chosen and must use powers to overthrow despotic government.”

It sounds silly, but it’s true. Online film and TV provider Netflix is widely known for its extremely comprehensive search algorithm that allows it cater to the most specific and baffling of tastes with startling accuracy. The formula—which was further enhanced thanks to a million-dollar contest promoted by the company—has won Netflix millions of subscribers and allowed them to dominate the subscription TV market. But some companies are looking to apply this model on an even bigger scale. is a new contender in the world of online multimedia entertainment provision (the service concluded its Beta test in December of last year). The company gives customers online access to movies, music, games, books and audiobooks for a monthly fee. Playster’s slogan, “Entertainment Unlimited,” promotes their niche of cross-media entertainment. The service hinges on the idea that, like genres situated within specific media types, the boundaries between media types themselves could be blurring—and they see this as a huge opportunity.

As a service, Playster is all about bundled content and the potential that it can offer. They want to provide users with a service that caters to all interests at once, whether that’s recommending movies based on someone’s literary tastes, or music that would pair well with an all-night gaming session. If genres within media types are collapsing due to the ever-expanding potential of recommendations algorithms, Playster argues, who’s to say the divisions between media types wouldn’t buckle under the same kinds of pressure? It could even work to the benefit of content marketers. While it’s perhaps easy to imagine Playster as a rabble of different media types clamoring for attention, the multimedia nature of the service actually provides an exciting opportunity for targeted marketing and cross-promotion—”people who read this book liked this movie and these games” and so on. It’s a way to reach new audiences.

Songza is a good example of a service that’s already doling out content to users in a way that, while still tailored, doesn’t rely wholly on genre; the service provides thousands of playlists organized by mood. Whether you’re cleaning the house or throwing a 1920’s themed Gatsby party for your book-loving friends, Songza has a playlist that unites different genres thematically to serve up an appropriate soundtrack. It’s a different approach to content organization that could signal a shift towards more lifestyle-based content suggestions for users.

Could a similar mood-based recommendations model be applied to e-books and audiobooks, then? Amazon currently suggests books to readers based on a combination of sales data and user-provided reviews and star ratings, but readers have complained that it still can’t reliably serve up relevant content. One of the potential reasons for this problem is that, unlike songs, TV shows or even movies, the medium requires a significant time investment, as well as a financial one, so when it fails, the impact is more deeply felt.

While all music services give blanket access to the catalogue for one subscription fee (Songza is actually free), books services are different. Bookbub, for example, rotates its content, so users only get access to a limited selection of the site’s total catalogue at any given time. Unlimited audiobooks services are especially rare, with most requiring the purchase of credits for each individual audiobook—Playster being the notable exception. The frustration, then, is that a dud book or audiobook recommendation actually makes a reader feel like they’ve wasted their time and money if they bought credits to consume it.

Lifestyle recommendations could work in some capacity for books, mainly for non-fiction titles—it’s easy to target healthy living books at fitness fanatics or market how-to guides on wealth building to aspiring businesspeople. Fiction is a bit harder to pin down. Offering snippets of books for free online could alleviate the problem, allowing people to read (or listen to) a few pages to see if a suggestion is actually relevant to their interests. Until a truly sophisticated algorithm comes along, though, the best way to find books to fit your tastes is probably through word-of-mouth, reading blog posts, and taking the time to browse.

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