Author Archives: Phil Barker

New feature: return RSS feeds for user

This feature allows a user to see all the feeds that they are filtering: useful if they just want to look at a specific feed or if they want to manage their feeds, e.g. by deleting one. If you’re interested you can read the original feature spec, though we haven’t implemented this in full (see comments).

The API call is an HTTP GET on [sux0rURL]/api/feeds/ (where [sux0rURL] is the URL for your sux0r installation, for this project that is http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/ ). The only parameter you can use is user= to specify the user name.

Example
So if you wanted to see the feeds that I have subscribed to you would GET http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/feeds/?user=philb which would return something like

<?xml version="1.0"?>
<rss version="2.0" xmlns:api="http://icbl.macs.hw.ac.uk/sux0rAPI/api/xmlns" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Philb's RSS Feeds</title>
    <link>http://icbl.macs.hw.ac.uk/sux0r206/user/profile/philb</link>
    <description>Use Case: Return the RSS Feeds for a User. User Nickname: philb</description>
        <atom:link href="http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/feeds/?user=philb" rel="self" type="application/rss+xml" />
    <item>
      <title>OUseful.Info, the blog...</title>
      <link>http://feeds.feedburner.com/ouseful</link>
      <guid>50</guid>
      <description>Thinking differently...</description>
    </item>
    <item>
      <title>Lorcan Dempsey's weblog</title>
      <link>http://orweblog.oclc.org/atom.xml</link>
      <guid>57</guid>
      <description>On libraries, services and networks.</description>
    </item>
<!--snip-->
    <item>
      <title>ehabitus</title>
      <link>http://ehabitus.blogspot.com/feeds/posts/default</link>
      <guid>53</guid>
      <description>(n). "e" + "a system of dispositions (unarticulated, habitual, acquired patterns of perception, thought and action)"</description>
    </item>
  </channel>
</rss>

i.e. an RSS feed where the items provide information on the feeds to which I am subscribed.

comments
Why an RSS feed? Well, we have the code return information in RSS feeds so why not? A simple extension would be to allow a parameter that would let one specify the format of the results, with OPML being the obvious alternative.

You’ll see that we’ve used the guid element to return the identifier used by sux0r for the feed (OK, we’re stretching the definition the “gu” part of guid). This can be used as the feed_id to identify the feed in other API calls, for example to return the items in that feed.

A PUT or POST (not sure which yet) to the same URL base would be a way of adding feeds.

Another extension would be to make the user parameter optional, returning information on all feeds in the system if no user name is provided–this could be useful for some admin functions.

Unfortunately we haven’t been able to implement the error codes properly on our server, you get an HTTP status code of 200-OK whether or not it is. However if you specify an invalid user name you do get sensible error messages returned in the body.

Even more unfortunately, the current implementation does not cover the authentication requirements.

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BayesFF: Final post

Diagram of prototype: schematically we can show how the prototype supports the aggregation and of RSS feeds comprising table of contents information from selected journals and filters them (using pre-existing software called sux0r) into two feeds, one of which is has information about those papers that are predicted to be relevant to a user’s research interests. The project has added the ability to interact with sux0r through third-party software.

Our work has shown how effectively this works for a trial group of researchers; in most cases, after sufficient training of the system, the outgoing feeds were successfully filtered so that one contained a significantly higher concentration of interesting items than the raw feeds and the other did not contain a significant number of interesting items.

End User of Prototype:
We have an installation of sux0r which people are welcome to register on and which can be used to set up feeds for aggregation (you will not automatically be given sufficient privileged to approve feeds, so it is best to contact the project about this). The base URL for the API for this installation is http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/ and the API calls which have been implements are documented in the following posts on this blog: Return RSS items for a user and ReturnVectors and ReturnCategories. Also available: a summary of other features for the API have been scoped. The latest update was 08 December 2009.

Here’s a screen cast of Lisa using the API


(NB the version at the end of the link is a whole lot clearer than the embedded YouTube version, especially if you click on the view in HD option).

The code for our work on the API is in a branch of the main sux0r repository on sourceForge.

Project Team
Phil Barker, philb@icbl.hw.ac.uk, Heriot-Watt University (project manager)
Santiago Chumbe, S.Chumbe@hw.ac.uk, Heriot-Watt University (developer)
Lisa J Rogers, l.j.rogers@hw.ac.uk, Heriot-Watt University (researcher)

Project Website: http://www.icbl.hw.ac.uk/bayesff/
PIMS entry: https://pims.jisc.ac.uk/projects/view/1360

Table of Content for Project Posts
Development work

User trialling

Community Engagement

Project Mangement

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noAuth

One of the “weaknesses” I put in the SWOT analysis was that we had a lot to learn. Fully understanding and implementing authentication and authorization for the API was one of the things that we had to learn. As of now, at the end of the funded work on the project, we seem to have failed in this.

Our first point of failure was in being pointlessly over ambitious in what we wanted to do via the API. When drawing up the initial feature set for the API I took the starting position that anything that you could do through the native sux0r interface should be doable remotely; so the feature set included register new user. This muddied the requirements for accessing the sux0r security procedures in a way that I can now see was quite unnecessary–it’s really not unreasonable to expect people to have an account with a service before the interact with it from another application.

Having clarified this it became clear that oAuth would be the authorization mechanism of choice, though we had no experience in implementing it. Santy got a client working with twitter and flickr based on Andy Smith’s library. He used
Google PHP OAuth library for the server on sux0r, but it didn’t work with either that client or Google’s own client. There is another library he would like to test for the server side, but had already spent more time than was available.

Struggling with oAuth meant less time to spend on actual features. In retrospect we should have implemented the features without authorization in the hope of adding some form of authorization later (which is indeed what Santy has done towards the end of the project), but it is always tempting to keep trying one more thing in the hope that the next try will succeed.

As a result we have fewer features implemented than we planned, and features that should require authorization don’t have it. We still hope to add some form of restriction on access, even HTTP digest authentication requiring sux0r user name and password to be entered into the third-party app is better than nothing.

Lessons learnt: 1) you don’t have to do everything through an API (god, that seems obvious when I write it); 2) get on with what you can do in parallel to trying to overcome road blocks; 3) analysing the problem and implementing the client did give us a better understanding of what oAuth should do.

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Features: ReturnVectors and ReturnCategories

The Return Items for a user feature assumes that if you want to get only those items that have been classified under a certain category you know the numerical code used by sux0r to identify the vector and category. These features allow you to find those codes.

Return vectors for a user
The full design for this feature is available, however the current implementation does not cover the authentication requirements.

The API call is an HTTP GET on [sux0rURL]/api/vectors/ (where [sux0rURL] is the URL for your sux0r installation, for this project that is http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/ ). The only parameter is user= to specify a username.

examples
HTTP GET on http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/vectors/?user=philb will return a list the vectors used by philb (me). The data returned is pretty self-explanatory, in this case you get:

<?xml version="1.0"?>
<response xmlns:api="http://icbl.macs.hw.ac.uk/sux0rAPI/api/xmlns">
  <api:userNickname>philb</api:userNickname>
  <api:vectors>
    <api:vector>
      <api:vectorID>6</api:vectorID>
      <api:vectorName>WorkInterest</api:vectorName>
    </api:vector>

    <api:vector>
      <api:vectorID>33</api:vectorID>
      <api:vectorName>CETIS-Domain</api:vectorName>
    </api:vector>
  </api:vectors>
</response>

Return vectors for a user’s category

The full design for this feature is available, however the current implementation does not cover the authentication requirements.

The API call is an HTTP GET on [sux0rURL]/api/categories/ (where [sux0rURL] is the URL for your sux0r installation, for this project that is http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/ ). There are two required parameters
user to specify a username
vec_id to specify the id of a vector used by that user.

examples
HTTP GET on http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/categories/?user=philb&vec_id=6 will return a list the categories used by philb (me) for the vector with id number 6 (which is “work interest”). The data returned is pretty self-explanatory, in this case you get:

<?xml version="1.0"?>
<response xmlns:api="http://icbl.macs.hw.ac.uk/sux0rAPI/api/xmlns">
  <api:userNickname>philb</api:userNickname>
  <api:categories>
    <api:vector>
      <api:vectorID>6</api:vectorID>
      <api:vectorName>WorkInterest</api:vectorName>
    </api:vector>

    <api:category>
      <api:categoryID>12</api:categoryID>
      <api:categoryName>interesting</api:categoryName>
    </api:category>
    <api:category>
      <api:categoryID>13</api:categoryID>
      <api:categoryName>not interesting</api:categoryName>

    </api:category>
  </api:categories>
</response>

Error trapping
Unfortunately we couldn’t implement the error codes properly on our server, you get an HTTP status code of 200-OK whether or not it is. However if you specify an invalid user name or vector id you do get sensible error messages returned, which include links to set you on the right track.

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Feature implemented: Return RSS items for a user

The single most important feature that we are adding with this project is the ability to publish feeds from sux0r corresponding to specified criteria, for example a feed aggregated from all the feeds that a user is subscribed to that have been classified under the same heading by the Bayesian algorithm. (Here’s the full specification if you’re interested). We have now completed work on this.

The API call is an HTTP GET on [sux0rURL]/api/items/ (where [sux0rURL] is the URL for your sux0r installation, for this project that is http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/ ). The parameters you can use are:
user to specify the user name;
vec_id to specify the vector id;
cat_id to specify the category id;
feed_id to specify the id or URL of the feed;
keywords to specify any keywords for filtering the result feed;
threshold to specify the threshold value for the probable relevance against the category;
maxHits to specify a maximum number of hits to return.

Sorting wasn’t implemented, the default sort order is on date. Also we didn’t get authentication working (but we dithered about whether it was necessary for this feature anyway, and life is easier if you can just get a feed into any feed reader).

Examples:
http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/items/?user=philb&maxHits=20
Gives the most recent 20 items from all the feeds to which user philb (that’s me!) subscribes. (I should note that not many of the feeds I subscribe to are Journal ToCs, so I’m not really using this for the type of feed for which it was intended. Nevertheless I find it kind of works.)

http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/items/?user=philb&keywords=jisc&maxHits=20
Gives the most recent 20 items containing the word jisc from all the feeds to which I subscribe. Try changing jisc to jisc cetis or “jisc cetis” or “jisc AND cetis”.

http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/items/?user=philb&vec_id=12&cat_id=24&threshold=0.5&maxHits=30
This is more interesting, vector 12 is my vector for classifying relevance to my research interests and category 24 is the stuff that is relevant. So this a feed of the stuff that is predicted to be relevant to my research interests (since the probability threshold is set to 0.5).

The results feed for that last call looks like this:

<?xml version="1.0"?>
<rss version="2.0" xmlns:api="http://icbl.macs.hw.ac.uk/sux0rAPI/api/xmlns" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Philb's RSS ItemsVector ID: 12, Category ID: 24, Threshold: 0.5, maxHits: 30</title>
    <link>http://icbl.macs.hw.ac.uk/sux0r206/user/profile/philb</link>
    <description>Use Case: Return the RSS Items for a User. User Nickname: philb. Summary of applied filters:  Threshold: 0.5;  maxHits: 30 results</description>
        <atom:link href="http://icbl.macs.hw.ac.uk/sux0rAPI/icbl/api/items/?user=philb&amp;vec_id=12&amp;cat_id=24&amp;threshold=0.5&amp;maxHits=30" rel="self" type="application/rss+xml" />
    <item>
      <title>An infrastructure service anti-pattern</title>
      <link>http://blog.paulwalk.net/2009/12/07/an-infrastructure-service-anti-pattern</link>
      <guid>http://blog.paulwalk.net/2009/12/07/an-infrastructure-service-anti-pattern</guid>
      <description>Last week I outlined an idea, that of the service anti-pattern, as part of a presentation I gave last week to the Resource Discovery Taskforce (organised by JISC in partnership with RLUK). The idea seemed to really catch the interest of and resonate with several of those members of the taskforce who were present at [...]</description>
      <pubDate>Mon, 07 Dec 2009 10:37:05 EST</pubDate>
      <source url="http://blog.paulwalk.net/feed">paul walk's weblog</source>
      <api:relevance>1</api:relevance>
    </item>
    <item>
      <title>Statistics of user trial results</title>
      <link>https://bayesianfeedfilter.wordpress.com/2009/12/07/statistics-of-user-trial-results</link>
      <guid>https://bayesianfeedfilter.wordpress.com/2009/12/07/statistics-of-user-trial-results</guid>
      <description>We now have results from our user trials showing how effective sux0r may be in filtering items from journal table of contents RSS feeds that are relevant to a user’s research interests. Quick reminder of how we ran the trials: 20 users had access to sux0r for 4 weeks to train the analyser in what [...]</description>
      <pubDate>Mon, 07 Dec 2009 07:41:18 EST</pubDate>
      <source url="https://bayesianfeedfilter.wordpress.com/feed">Bayesian Feed Filter</source>
      <api:relevance>1</api:relevance>
    </item>
<!--lots more items-->
  </channel>
</rss>

Apart from an additional element for the relevance of the item to the specified category, it’s plain RSS 2.0.

Unfortunately we couldn’t implement the error codes properly on our server, you get an HTTP status code of 200-OK whether or not it is. Also, I think there are some error conditions that we don’t trap satisfactorily, for example specifying a non-existent user or category.

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Statistics of user trial results

We now have results from our user trials showing how effective sux0r may be in filtering items from journal table of contents RSS feeds that are relevant to a user’s research interests.

Quick reminder of how we ran the trials: 20 users had access to sux0r for 6 weeks to train the analyser in what they found interesting and not interesting. We then barred access for 4 weeks but continued to aggregate feeds and filter them based on that training. Then we invited the users to look at the results of the filtering: two feeds from sux0r; one aggregating information about journal articles that had been published while the users were barred that sux0r predicted the user would find relevant; the other feed had information about the rest of the articles, the ones that sux0r predicted the user wouldn’t find relevant. We had our users look through both feeds and tell us whether the articles really were relevant to their research interests. We lost two triallists and so have data on 18, you can see this data as a web page (or get the spreadsheet if you prefer).

The initial data needs a little explanation: The first columns (in yellow) relate to the number of items used in the initial six weeks to train the Bayesian analyser in what what was relevant to the users research interests, what wasn’t, and the total number of items used in training. The “Additional docs” column relates to information added that didn’t come from the RSS feeds: was asked users to provide some documents that were relevant to their research interested for training in order to make up for the fact that in a fairly short trial period the number of items published that were relevant may be low.

The next set of columns (in green) relate to the feed of items aggregated after the training (while the users had no access) that were predicted to match the user research interests, showing the number of items of interest in that feed, the total number of items in that feed and the proportion of items in the feed that were interesting. The next three columns (in red) do exactly the same for the feed of items that were predicted not to be relevant.

For a quick overview of the results, here’s a chart of the fraction of interesting items in both feeds:

You need to be careful interpreting this chart. It hides some things, for example, the data point showing that the fraction of interesting items in one of the feeds was 1 (i.e. the feed of interesting items did indeed only have interesting items in it) hides the fact that this feed only had 2 items in it; the user found 9 items overall to be relevant to their research interest, 7 of them were in the wrong feed. Perhaps that’s not so good.

So, did it work? Well, one way of rephrasing that question is to ask whether the feed that was supposed to be relevant (the “interesting feed”) did indeed contain more items relevant to the users research interests than than would otherwise have been the case. That is, is the proportion of interesting items in the interesting feed higher than the proportion of interesting items in the two feeds combined. The answer in all but one case is yes; typically by a factor of between two and three. (The exception is a feed which achieved similar success in getting it wrong. We don’t know what happened here.)

Also we can look at the false negatives, i.e. the number of items that really were of relevance to the user’s interests that were in the feed that was predicted not to be interesting. The chart above shows quite nicely that after using about 150 items for training this was very low.

What about some statistics? It’s worth checking whether the increase in concentration of items related to a user’s research interest as a result of filtering is statistically significant. We used a two sample Z test to compare the difference in the proportion of interesting items in the two feeds to the magnitude of difference that could be expected to happen as the result of chance:
.

I have some reservations about this because of the small number of “interesting” items found in the feed which should be uninteresting when the filtering works–this means that one of the assumptions of the Z-test might not be valid when the filtering is working best–but any value of Z above 3 cannot be reasonably expected to have happened by chance.

Conclusion: for users who used more than about 150 items in training the filtering produces a statistically significant improvement in the number of items in the feed that were relevant to the user’s research interests without filtering out a large number of items that would have been of interest. Next post: were the users happy with these results?

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User activity

One indirect measure we have of the level of engagement from our trial users is how often they signed into the system looked at their feeds and did some training. Some analysis of the sux0r logs gives the following chart of activity with date (each colour represents a different user):

There was obviously a lot variation between users in how much they used the system (more on that very soon) but what I like from this graph is that for several users (about a third of them) it shows continual spontaneous use throughout the trial period, not just at the points when we were pushing them.

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