Ask HN: How do you get articles/papers/talks recommendations?

[__all__]

I've been trying different approaches such as subscribing to newsletters or using RSS readers to follow people I find interesting but not everything they write every time is interesting! Also, if you limit yourself to following people you've been reading before you probably end up not discovering new content.

With the technology, amount of data, and algorithms we have currently should be pretty easy to build a tool capable of tracking articles/papers/talks you read/like and suggest things you could be interested in.

Does anybody use/know a tool like that? There are many places to get suggestions based on how popular they are across the entire platform but aren't we in the era of the personalization?

[rsfern]

I really like arxiv-sanity. I browse the trending lists and also like to surf the “similar papers” links

I wish there was something similar for the general literature, it only indexes like the ML and vision related communities

http://www.arxiv-sanity.com/

[jarvist]

There is something general: https://arxivist.com/

Interface is much less polished than ArXiv-sanity. It can also send a daily email of the top 5 for you, which I find very useful.

[rsfern]

Cool service, thanks!

I suppose I should also elaborate, one hangup that I have is that my primary field (materials, specifically alloy design) doesn’t really have a strong preprint culture. So access to fulltext to build something like this covering non-ML-adjacent materials research is a real problem.

I’ve thought about pulling titles and abstracts from crossref or something, but I’ve never really gotten around to trying to make it with in earnest

[jldugger]

> How do you get articles/papers/talks recommendations?

For papers, mostly through social sharing I think. HN, reddit, and slack communities. For videos, I subscribe to a few youtube channels, like PapersWeLove. Reddit has /r/contalks as well for a broader list.

The rest is second order effects. If in the course of reading an article, I notice the author because I've seen another article from them I like, I'll subscribe to them. Bloggers have RSS feeds, and even researchers like Cormac Herley can be followed via Google Scholar. Or if a conference seems to produce a lot of interesting research, I'll add it to my calendar for next year to review next year's crop of papers. And the stuff I read typically has references, so if a subject interests me, I'll typically glance at the citations as I encounter them and put interesting ones in my backlog.

> a tool capable of tracking articles/papers/talks you read/like and suggest things you could be interested in. Does anybody use/know a tool like that?

Youtube does this. I get recommendations for USENIX talks that are fairly newly uploaded, because ive watched a few in the past. This is actually useful since sometimes USENIX uploads videos years after the conference; I seem to be getting a few recs from conference recordings taken years ago but uploaded this week.

I'd imagine Mendeley and other citation managers could also help here, but it's pretty niche and as a not-professional researcher, adopting these for professional use is far down my backlog.

[btrettel]

Google Scholar alerts have been pretty useful for me. Most of my alerts are for papers that cite others, but I also have some alerts for normal search queries.

[jarvist]

The issue that I find with Google scholar is that it seems very biased by my publications. So it's very good at telling me the latest work related to what I used to work on, but not necessarily what I am currently interested in.

[btrettel]

I think you're referring to a different Google Scholar feature than what I was referring to.

Google Scholar will periodically email me a researcher their AI thinks does work similar to me. For the most part this is close but not quite right. It sounds to me like this is what you're referring to.

I set up manual Google Scholar alerts for citations to papers of interest to me and some particular search queries. Many of these are unrelated to anything I have published, and they don't depend at all on my publications. I've heard of many interesting papers through these alerts.

[jean_valje4n]

I recommend searching for stuff in google scholar, taking into account how many citations a paper has had by other papers. This isn't foolproof, but one can read a lot of interesting primary literature that way.

Also look for review articles that summarize the studies of multiple primary literature. Review articles are amazing in that they can sum up a lot of different ideas with citations to the primary work of others.

[Vervious]

It seems to depend on how specialized you are. I bet it would be very difficult for any automated tool to understand how good a paper is. Until then, you have to rely on other people who understand the paper, to get good paper recommendations. Most papers are incredibly specialized and I bet can only be totally understood by a few dozen/hundred/thousand people (depending on field?). In other words, if you are a student, ask your advisor/professor. If you are looking for general papers to read about systems, someone's probably written a blog post about it. Or popular science magazines (e.g. Quanta) might be nice.

Oh, or you can browse conference proceedings of the top conferences (in CS, at least.) (Though those are also, gigantic, and you probably want to filter even further...)

I feel that Google Scholar Alerts are only useful once you can filter paper titles / taglines by yourself, which requires tremendous expertise. I would be very surprised if any automated tool could replace other people and their technical expertise (which took years of training to develop) as a paper filtering tool. Otherwise you might as well automate peer review.

[Jugurtha]

I get my recommendations from the content I read. Someone in a talk mentions an article/book/person, I quickly take a note with the context (from video URL, interview with X, mentions Y, or book, or something). I use TaskWarrior.

[lonelyasacloud]

Different domain, I know. But, fwiw, the best personalised content recommendation thing I've found that did a good job of both finding new related content _and_ busting siloes was the Last.fm music social network/streaming service.

Last.fm used you've got X in your library/listened to X, other's into X also liked Y algorithm to suggest and stream a mix of music you might like. It was sufficiently good that in 2012, they had to admit that 43 million of their accounts had been compromised when they were hacked. Their original business model eventually failed, and in 2014 they ended up removing streaming. And with that, it effectively became a music version of Good Reads. Which, although it is still going, killed it for most people.

Anyway, there's quite a bit of quality writing about Last.fm and its demise, e.g. https://www.washingtonpost.com/news/arts-and-entertainment/w... , https://www.vice.com/en/article/a37x9g/lastfm-was-the-only-m.... For my money, Last.fm must be about one of the best case studies into what works well and what does not. I would have a read if I were thinking about something similar.

[lonk11]

Let me plug my hobby project: https://linklonk.com - it is a collaborative RSS reader and I did a Show HN recently for it: https://news.ycombinator.com/item?id=29445764

I think it may strike the balance you are looking for between: 1. following popular content, 2. subscribing to individual sources and 3. getting personalized algorithmic recommendations.

From the Show HN post:

"LinkLonk is a novel mechanism to subscribe to RSS feeds and discover content - upvote or submit a link to anything you liked and you will get connected to RSS feeds that posted this content. The more content you upvote from the same feed - the higher other content from that feed will show up in the For You page. This helps you see content from feeds with the highest signal-to-noise ratio first.

In addition to RSS feeds, you connect to other users who upvoted the same content as you. This way other users help highlight great content from feeds you are already connected to and discover other feeds that they are connected to.

To sum up: upvote content => connect to RSS feeds and users => discover great new content => repeat."

[mindcrime]

For me it's a combination of sources:

1. Links posted on HN

2. Links posted in various sub-reddits (/r/machinelearning, /r/semanticweb, /r/artificial, etc.)

3. Links posted in a variety of Facebook groups I follow

4. Links posted on Twitter by people I follow

5. Google Scholar alerts via email

6. Ones located as references or "external links" on Wikipedia pages

7. Manual searches or browsing of arxiv.org, jmlr, aclweb, aaai website, etc.

8. Google / Google Scholar searches for certain keywords or phrases

9. Ones mentioned in books or cited in other papers

[ssss11]

Every tool I’ve seen in the internet trying to profile me and give me suggestions fails miserably. The tech isn’t there and the companies aren’t trustworthy enough for me to say “yes have my data so you can build a profile of me!” and expect them to respect my data.

I just follow HN and a couple of other places that provide good content.

[freediver]

I wasn’t happy with anything out there so I built TinyGem [1] whic not only aggregrates content from all over the most relavant plaves on the web (including research papers) but it also provides machine learning-based recommendations.

[1] https://tinygem.org

[__all__]

I've been trying TinyGem and the Discover section is always empty :(

[AHASIC]

didn't work for me when I typed 'svelte'

[freediver]

It means there is nothing new worth reading today about ‘svelte’ and that is OK.

[exaltation]

Seconding the recommendation for Thinking About Things [0] and Findka Essays [1].

[0] https://www.thinking-about-things.com

[1] https://essays.findka.com

[estoilkov]

In the AI/CS fields you need to check: https://www.techontheedge.com

This is a complete solution for web and mobile, aggregating articles, news and papers from 1000ds of sites across the web.

You will find:

  • Latest news, articles, and research in AI, ML, DL, NLP, IoT, Quantum, Web, Mobile, careers...
  • Curated news feeds for many AI/computing related topics
  • Keywords search is central, personal lists of topics, sites, and rss feeds
  • Preview or listen to summaries, save and share links
  • Newsletters for personal keywords and sites
  • Only articles from the last 2 days

[nothrowaways]

For papers in computer science repo below lists a good number of resources

https://github.com/papers-we-love/papers-we-love

[rjtavares]

Since (for now) communities and curation beat algorithms, Reddit is a great way to discover content. Just find an interesting subreddit and view the top posts of the month/year.

[eatonphil]

I tried to do a curated weekly online tech talk blog series but I just couldn't find the right way to build an audience. Or maybe I gave up too early. This weekly announcement of curated talks is something I always wanted and I'm sure others would want it too.

https://datastation.multiprocess.io/blog/2021-07-12-this-wee...

[ReleaseCandidat]

I start my 'brwosing' at Arxiv https://arxiv.org/ You always find references to other papers, so in the end it doesn't really matter where you start your search.

The main problem for me often isn't finding interesting stuff, but papers that I can read for free.

[morphle]

Almost all papers are free at https://sci-hub.se.

Google Scholar searches give the links to free papers at the rightmost collum

[sillysaurusx]

From gwern.

https://reddit.com/r/mlscaling

[peter303]

You have to discover some "trusted curators" bloggers or columnists who make suggestions.

For example when libraries closed their new book shelves for a years due to covid fears, I started relying on NYTimes Sunday Book Reviews of non-fiction for good choices.

[DrNuke]

Use Twitter to follow the most widespread tech magazines maybe? Wired, IEEE Spectrum, Quanta Magazine, and so on. You will get a comprehensive number of articles and references about the hottest takes and the flavors of the month.

[dredmorbius]

I follow a more traditional literature-search method, though it's augmented with online sources.

I start with a question. Mine initially was "What are the Big Problems?" I then dive recursively into that.

Starting reading at virtually any point, you'll discover that knowledge is a web, and that the best authorities reference others. Then the spidering begins.

Follow an author's references. If you find a quip or fact or quote or reference which seems especially germain, then look it up. Given today's Internet, this oftem means you can have a specific reference in front of you in seconds. I had this experience re-reading James Burke's Connections a few years ago, in which he mentioned Agricola's De Re Metallica (which is not about the band), and found that the English translation of this 16th century work (not completed until the early 20th century) was at the Internet Archive. (The translators also have interesting biographies.)

If you find an especially good source, look to see what other sources reference it. That is, look for citations. This is slightly less powerful than the first method, but 1) serves as a check to see what works are truly significant (they'll have high citation counts) and 2) will lead to more current treatments of a concept (which aren't always better or improvments, mind).

Those are the two principle methods.

Once I find an author or topic (subject heading or keywords) of interest, I'll use a traditional catalogue, almost always Worldcat, to look for additional materials. If you find an author of interest, this is a good way of finding their other works. Worldcat indexes both books and articles.

https://worldcat.org/ DDG bang search !worldcat 'au:' == author, 'ti:' == title, 'kw:' == keyword

I don't have a good catalogue for popular magazine or newspaper articles, though there are several commercial options. Some libraries (public, community college) will provide access to these. Google Books captures some of this material, at least for searching.

Google Scholar, Archive.org, Open Library, LibGen, and ZLib are also useful for both searching and sourcing documents.

General Web Search has become all but useless over the past 5 years or so.

Finding an idea, especially one that seems to be universally accepted and unquestioned, and seeking out its source can be profoundly interesting. Google's Ngram Viewer is your principle tool here, as you can see specifically when a specific word or phrase (up to five words) emerges. Quite often "accepted wisdom" is found to emerge with very little empirical foundation. It can be tricky to identify where the breakout occurs and through what work, but this approach seems to work better than others.

Online sources are another option, though what I increasingly find is that more-recent online content tends strongly toward lower value, and less use of these is better. This depends greatly on the field. Among the best options is to not read the current submissions, but to do a specific search for top items within some time bound.

On HN, you can effectively see the top submissions from the past week, month, or year. I've addressed that here:

https://news.ycombinator.com/item?id=28806795

Other sites, notably Reddit, have similar date-bounded search options. Incidentally, if you're assessing whether or not a subreddit is worth subscribing to, reviewing its top posts by week / month / year is useful.

In general, I find that identifying a good author or publication, and "stalking" their output, is superior to virtually any user-generated content site (FB, Reddit, Twitter, HN, etc.).

Books and articles have higher hurdles to publication than online articles do. The Internet's editorlessness is becoming more of an obstacle than a benefit as there is simply so much crap online.

Track your references. Zotero seems to be the gold standard here, though I don't use it myself. Calibre has its uses. Avoid Mendelay like the plauge it is.

Consider a Zettelkasten or equivalent. I'm referring to pen-on-paper index cards as the most robust option here, though there are digital versions. Of these, I'd strongly recommend Emacs org-mode or a flatfile ASCII / UTF-8 reference as the most robust, possibly a wiki. The simpler and more robust this is, the better, as it will quite possibly last your entire life. The problem with hot new software is it often does not.

I strongly recommend an e-book reader or tablet with the absolute most onboard storage you can manage. I'm pretty happy with the Onyx BOOX line, and have their largest device, the 13.3" Max Lumi. It's been updated recently to 128 GB onboard storage (mine is 64 GB, and I'm bumping up into that), and I'd prefer that were bumped to 1 TB (some Apple iPads reach this).

I strongly prefer e-ink to emissive displays.

For size, 6" is about the minimum size you should consider, 8" is comfortable for most straight text or e-pubs, 10--13" is much better for scanned-in PDFs of older works and articles in particular. (That was my thinking in buying the Max Lumi, and it's largely been validated.)

My usual problem isn't to little to read, but far too much, and setting (and sticking to) priorities on that.

[dredmorbius]

One more new materials source: the New Books Network family of podcasts.

https://newbooksnetwork.com/

Each podcast channel is a set of reviews of new academic publications within a given area. (Books and interviews may feature on multiple channels.) If you're interested in keeping abreast of recent developments across a range of topics, they're a great way to dip your toe in the water.

Production qualities vary tremendously, and yes, there are unlistenable interviews. That said, there are also many excellent ones, and the low production values conceal a great trove of valuable material.

[Zacny_Los]

I like also https://essays.findka.com/

[satisfice]

I use Hacker News.

Otherwise I do searches using Publish or Perish and collect the the papers into Zotero using Sci-Hub.