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enterprise search (especially in combination with bad information
architecture) is the no. 1 frustration for intranet users in most organisations. Even
though there is quite a number of things you can do to make search
better (see for example DWG’s research report “Improving
Search“, available for DWG members only), in most organisations
there still is ample room for optimisation.
bad search is (in most cases) not a technical issue, it seems only
natural that social enhancements taken from the Web 2.0 space should
provide chances for improving search experience and results.
take a quick look at some such options:
user generated context
users can add context to content by tagging, setting bookmarks,
rating contents or voting on search results, all these additional
meta-data can be used in optimising the relevance ranking of a search
people interact with content (e.g. bookmark it), this content
normally is of importance to them (or of no importance, if for
example a very low rating is given). Thus e.g. keywords associated
with a content by social tagging typically express a higher
relevance than keywords that have to be provided by the author of a
contributions relevant to a search term can also be used to provide
‘people results’ to a search thus not only delivering relevant
content but also people potentially related to the topic searched
a sufficient number of contributions across the whole spectrum of
content available, there’s a high risk of falsification of the
search results (e.g. content that is important for a topic is
displayed far down in the search results just because it has no
ratings or tags yet)
for a search term can be very different for different audiences,
thus search results can become (falsely) biased to audiences that
are more active in contributing that others
tags can provide ‘synonyms’ that might not be available in the
content (and meta-data) itself. Thus more potentially relevant
results for a topic can be found (which of course is a ‘pro’).
Technically speaking the ‘recall rate’ of the search is improved.
Higher recall (more results) automatically means lower precision
(relevance of result ranking), though. For more, see: recall
Lists / Topic searches
user A (or team A) has researched a topic intensively and put
together a list of the most relevant sources for this topic, then
user B can be provided with an excellent starting point if looking for
information on the same topic by accessing this ‘search
Examples for this kind of search support can be found on
the web at Rollyo or Swicki.
are restricted (or focussed) to sources of potentially high
relevance, thus eliminating large numbers of probable irrelevant
synergies by ‘reusing’ efforts already put into researching topics
experts for topics
more dynamic information on a certain topic is, the bigger the risk
that the sources included in the list don’t fully cover everything
that might be of relevance to it (e.g. new blogs on the web come up
by the minute thus making it impossible to provide comprehensive
coverage in static lists).
Thus potentially relevant results
might be missing if the search lists don’t contain everything
related to the topic.
see relevance for different audience above
associating the ‘best’ result for a certain search term is a powerful
way of improving relevancy for the most sought after topics. This is
typically done by a central team that analyses the most frequently
used searches. With ‘social best bets’ everybody can contribute his
favourite search results in regard to a specific search inquiry.
example Google has integrated this concept as ‘Wiki
KeyMatch‘ into the Google Search Appliance.
centrally maintained best bets are usually limited to no more than
around the 100 most frequent or important search terms (due to the
time and resources required for maintenance), social best bets can
cover much greater number of search terms and a wider spectrum (e.g.
for very specific themes).
also pros above
important for user A doesn’t necessarily have to be of relevance to
user B. If social best bets are displayed on top of the result list,
users will get quite frustrated with this feature if it repeatedly
delivers ‘bad’ suggestions.
This issue can (partially) be
addressed by enabling users to rate on the helpfulnessÂ of best bets
and adjusting its relevance accordingly.
also cons above
2.0-style designed search results pages (SERPs)
of displaying a single search results list, you can think about
designing the SERP in a web 2.0 portal fashion. You might consider
displaying elements like:
‘boxes’ for different search sources (e.g. displaying search results
from internal blogs in one box, those from regular intranet
content in a separate box, results from wikis in yet another etc.)
alternate ways to navigate the content returned e.g. with tagclouds
(e.g. like Quintura does),
providing lists of what search results other user have clicked on
for the same search or results that other users have voted as most
relevant to them (e.g. like at Xibben). Some people decide to make use of the anchor text in their SEO stratergy to boost their search result rankings on external search engines like google and internal searches conducted by users. If you’re interested to learn more read this article on what is anchor text from Serp.co.
picture and video results by displaying thumbnails (where
adding human touch by returning people with photos that are related to a
search (e.g. that frequently use a tag that was search for by the
can be presented in a more encompassing and differentiated way thus
potentially serving the needs and ways of working of more audiences
a design that is more pleasurable to use and stimulates more
not well designed such pages can quickly become cluttered and
are accustomed to google-style SERPs and might be reluctant to get
used to anything that looks different
none of the options outlined is a magic pill to solving all problems
with internal search, it sure is a good idea to think about how they
could benefit your organisation and what existing issues and user
requirements they might address.
there are more ‘social’ ways for improving enterprise search than
just the ones sketched above. I’d love to hear from any other ideas
that you have already experimented with.