241–250 of 292 posts
Earlier quoted context omitted.
Try Google voice search; I just say "OK Google -- navigate to home".
I don't want my phone listening for key phrases. This was a solved problem years ago. Windows Phone let you pin nav directions to any address you wanted directly on your home screen.
https://support.google.com/maps/answer/6291823?co=GENIE.Plat...
The artistry of writing about how Goodreads is broken, on Medium, which is completely broken. Well done, well done.
Earlier quoted context omitted.
Entering "1984" yields "books similar to 1984 by Michael Dean". Entering "Nineteen Eighty-Four" gives me "Book picks similar to Nineteen Eighty-Four by Matthew Dunster". When I search for "Nineteen Eighty-Four: A Novel", the site responds with "Book picks similar to Beer in the Snooker Club by Waguih Ghali". I'm sure Dean, Dunster, and Ghali are terrific writers but what happened to George Orwell?
"1984 orwell" finds it. Just "1984" looks like it's finding a translation or something - the cover picture clearly shows George Orwell. Maybe Dean is the translator? Still a bit odd.
> by Michael Dean (Adaptation), Andy Hopkins (Editor), Jocelyn Potter (Editor)
Also in tiny text below the description:
> Paperback, 71 pages
...that seems really short. Not sure this is the full novel.
Earlier quoted context omitted.
Right. You only have four actions on Spotify as I recall (thumbs up, thumbs down, pause and fast forward). I guess some of the other apps have the larger bucket of 'who is watching/acting' based on account. I am not sure how many actions are needed, but I can think of a few more: * Not now, but later is fine * Maybe next month * Play the opposite of this * Something similar but new to me
It'd be nice to have an "I've already seen this" button that will mark an episode/season/show as watched so it doesn't keep coming back up. Netflix really wants me to watch Breaking Bad, but I've already seen it. I liked it; give me more like that, but not exactly that
That's not just nice, that is the most obvious "feature" a normal person would design. I've stopped watching Netflix because of nonsense like this. They know I've seen it before, because I watched in their player. Also, even the tiniest, most modest application of analytics would discover that I always watch one series at the time, from start till end, or whenever I'm bored with it. In the past years, I have never ever watched a thing twice. Still, it was constantly showing me things I've already seen.
The only conclusion I can draw from this is that Netflix does not have have my best interest in mind when designing their algorithms and that they don't respect their customers. So I canceled.
> The recommendations suck, the lists suck — it’s like, 100 lists telling me to read The Handmaid’s Tale and Harry Potter. I had the same experience with GR and also Amazon.com which constantly peddles the vampire romance books when I am looking for recommendations for horror/fantasy. Both Amazon and GR strategy make sense because best-selling books sell the best, so they should recommend them to increase profits. Ho…
Thanks for making this! I tried a bunch of searches and the recommendations are surprisingly good. A couple of points. All my searches selected an (in my opinion) less prominent result as the top hit and I had to click the "see other results". The searches I remember doing were "Blood Meridian" (should show Cormac McCarthy as top hit and "Waiting for the Barbarians" (J. M. Coetzee). Secondly, I'm not sure if it's sen…
Earlier quoted context omitted.
Calibre can download metadata and covers from a number of sources, mine lists: Amazon, Big Book Search, Edelweiss, Google, Google Images, OZON.ru, Open Library, and Overdrive. Of course I'm only searching for one book at a time.
but can you use these covers though, especially if you have ads on your site etc? aren't those images Amazon's property?
I read quite a bit and generally like Goodreads for what it is, but I agree that it's essentially not grown at all in 12 years. There are parts that are broken, there are features that are lacking, and it frustrates me to no end. Many, many times I've thought of how I'd build a competitor, but it is pointless because Goodread's moat is too big: the integrations with the kindle go a very long way towards cultivating e…
Honestly, I have been feeling that the integration with Kindle, and Amazon in general, is what killed it for me. They took an audience of bibliophiles (and their ratings) and expanded it to the mass market users, encouraging them to star and comment on what they just read. What I'd really like is more access to the data and filtering mechanisms to try to build recommendations for myself. Example: Find the highest rat…
People in many different jobs use Excel and SQL databases - I wonder if enough to make 'power user' but mass market apps with that sort of interface.
> The recommendations suck, the lists suck — it’s like, 100 lists telling me to read The Handmaid’s Tale and Harry Potter. I had the same experience with GR and also Amazon.com which constantly peddles the vampire romance books when I am looking for recommendations for horror/fantasy. Both Amazon and GR strategy make sense because best-selling books sell the best, so they should recommend them to increase profits. Ho…
As far as I can tell, all recommendations lists fail compared to freely-available experts. Like, if you like literary fiction, go through the Pulitzer prize for fiction, and just read. (I'm not even half way through that list, but everything I've read on it has been really, really good.) - there's all sorts of awards for smaller niches... the nebula, the Hugo, etc... (Actually, that's a question. What is the award fo…
Earlier quoted context omitted.
I build search engines for a living. While I appreciate the hacker spirit of your project with Solr, I also see this as a huge problem that leads to bad search experiences. Tuning boosts in Solr is not even close to a reasonable way to solve problems like this. Arguably not even for an underfunded library, but certainly not for a high web traffic consumer website. For one, you need disciplined acceptance criteria in…
But it works? If you measure your tuning based on which rank an item is when a user clicks you might get feedback that would obviously help find more examples or create some kind of self-learning system, but if you only care about say, boosting common best sellers periodically, well, isn’t that good for (almost) everyone? ;-) My suggestion for GoodReads would be to rank exact title matches higher than partial or reor…
This is the big red flag, when non-specialists hacking on Solr boosts are claiming something works because of a few qualitative test cases.
“It works” is a statement that only applies after you’ve done qualitative and quantitative goodness of fit testing.
You wouldn’t have a random IT employee make a stock-trading algorithm and then test it on a month of data and call it a success.
For a search solution to “work,” it needs to pass quantitative and qualitative checking, and be explainable to stakeholders and be reproducible / incrementally updateable. The training and arrival at hyperparameters all need to be reproducible and based on the outcome criteria they are meant to solve.
Making some hacks into a demo that superficially looks good is not at all the same as “it works.”
Earlier quoted context omitted.
My preference is to rely on the judgement of reviewers whose taste kind of matches mine. I'm into SF and Fantasy. In the past, I would read the reviews featured in Locus magazine by the various reviewers. Nowadays, I occasionally read Locus but also reviews from other places like tor com, the Magazine of Fantasy and Science Fiction and Interzone magazine.
> My preference is to rely on the judgement of reviewers whose taste kind of matches mine. I wish there were a website where I could put in all of my favorite video games, books, movies, anime, etc. And it would recommend me things based on what people with similar tastes liked. Then, I could try out the recommendation and then either like or dislike it. I would slowly acquire a recommendation network of people like…