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Lucene's New Analyzing Suggester

Live suggestions as you type into a search box, sometimes called suggest or autocomplete, is now a standard, essential search feature ever since Google set a high bar after going live just over four years ago.

In Lucene we have several different suggest implementations, under the suggest module; today I'm describing the new AnalyzingSuggester (to be committed soon; it should be available in 4.1).

To use it, you provide the set of suggest targets, which is the full set of strings and weights that may be suggested. The targets can come from anywhere; typically you'd process your query logs to create the targets, giving a higher weight to those queries that appear more frequently. If you sell movies you might use all movie titles with a weight according to sales popularity.

You also provide an analyzer, which is used to process each target into analyzed form. Under the hood, the analyzed form is indexed into an FST. At lookup time, the incoming query is processed by the same analyzer and the FST is searched for all completions sharing the analyzed form as a prefix.

Even though the matching is performed on the analyzed form, what's suggested is the original target (i.e., the unanalyzed input). Because Lucene has such a rich set of analyzer components, this can be used to create some useful suggesters:
  • With an analyzer that folds or normalizes case, accents, etc. (e.g., using ICUFoldingFilter), the suggestions will match irrespective of case and accents. For example, the query "ame..." would suggest Amélie.
  • With an analyzer that removes stopwords and normalizes case, the query "ghost..." would suggest "The Ghost of Christmas Past".
  • Even graph TokenStreams, such as SynonymFilter, will work: in such cases we enumerate and index all analyzed paths into the FST. If the analyzer recognizes "wifi" and "wireless network" as synonyms, and you have the suggest target "wifi router" then the user query "wire..." would suggest "wifi router".
  • Japanese suggesters may now be possible, with an analyzer that copies the reading (ReadingAttribute in the Kuromoji analyzer) as its output.

Given the diversity of analyzers, and the easy extensibility for applications to create their own analyzers, I'm sure there are many interesting use cases for this new AnalyzingSuggester: if you have an example please share with us on Lucene's user list (java-user@lucene.apache.org).

While this is a great step forward, there's still plenty to do with Lucene's suggesters. We need to allow for fuzzy matching on the query so we're more robust to typos (there's a rough prototype patch on LUCENE-3846). We need to predict based on only part of the query, instead of insisting on a full prefix match. There are a number of interesting elements to Google's autosuggest that we could draw inspiration from. As always, patches welcome!

 

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