Keyword Search, Associative Browsing and Faceted Navigation are three major techniques of information access. Google provide search, browsing (find-similar links) and navigation (filtering by time and types), and there exists many domain-specific collections of structured data (e.g., Amazon, IMDB and Yelp!) which supports all three methods in different forms. In this post, I’ll compare these method, and consider ways to combine these method in a single search scenario.

Comparing Three Methods

In essence, these three modes of information access can be considered as some kind filtering and ranking of given items. Search involves both filtering by query terms and ranking by textual match between items and query and possibly other criteria. Associative browsing provides ranking based on match between one item and others, and faceted navigation is filtering by a set of conditions defined in terms of metadata values. 

From user perspective, each of them requires different kind of knowledge and effort from the user. Search assumes that users can type in keyword, yet users can’t even start otherwise. Associative browsing assumes that user can choose a relevant item among suggestions, yet users have no control over which items are suggested. Finally, faceted navigation requires users to choose a facet of interest, which is not always easy if users do not have knowledge of the domain.

Consider an example of user trying to find a trail for hiking. If she knows of a right keyword, she’ll type them in to narrow down the candidates, or she might just pick one of criteria suggested in faceted navigation interface. After she found an appropriate trail, she can browse nearby trails by browsing feature.

Likewise, since each user might have different ability and preference, it will be helpful to provide all these mechanisms, and let the user to mix and match the methods as they want. An important question is: how can a system provide a reasonable combination of these methods?

Combining Three Methods: Traditional Ways

While providing three methods within a single system is a matter of implementation, a question remains about the sensible combination of three methods. Previous works on faceted navigation has been consistently arguing the importance of integrating faceted navigation with keyword search, where  the facet display is dynamically updated according to the set of items returned as search results. Also, most work on associative browsing assumes that keyword search is used as an entry point, so that users can subsequently browse into the item they desired starting from from search results.

Combining Three Methods for Exploratory Search

That said, FXPAL paper on exploratory search seems to open up a whole new set of possibilities for combining three methods based on selective application of contextual knowledge, i.e. queries and documents issued or judged so far.

Assume that users are willing to make judgments on which items were relevant or non-relevant. Although their original proposal was to use these judgments for refining keyword query, yet I think these set of documents can also be used to determine metadata values or ranges that can be used for faceted navigation. This will certainly ease the difficulty in specifying appropriate values for facets.

Going back to trail-finding example. Initially it might be hard for a user to know exactly what are appropriate length and difficulty of trail for her. However, after looking at several trails she liked, she now has better idea on those facet values. Although she can browse through all the items and manually update facet values, the system can make it much easier by automatically updating facet values based on trails she preferred. 

Looking Forward

The idea of combining multiple access methods is certainly appealing for users, because users can start searching regardless of what they know, improving their understanding along the way. If the system further let them use their knowledge for refining their expression of information needs, it will be even more powerful.