Providing spot-on results with good relevance in Enterprise Search solutions is one of the hardest tasks when working with findability. Sure, it is doable to work out a generic model for ranking results based on the organization’s most common requirements on findability in conjunction with available metadata of the information made findable. But is it enough?
The burning question is: How can you ensure that the generic relevance model does not get outdated once the Findability solution has been in use for a month, half a year, a year and the implementation crew is long gone?
Findwise recently released a large Enterprise Findability solution at a customer in the electrical power industry in Sweden. In the project we identified personalized and adaptive relevance as two key requirements for the findability solution to provide real, future-proof value-in-use to a large set of people with fundamentally different roles within the company. This blog post will focus on the latter requirement, adaptiveness: How can we make sure that an Enterprise Findability solution returns search results that become better and better as the solution is used?
Let user behavior improve the behavior of the search tool
The Enterprise Findability solution rolled out at the power company contains two features that, put together, build the foundation of a continuously improving relevance model:
- A feature that promotes popular content given a query term – “social relevance”
- A feature that continuously changes the relevance model by boosting the relevance of popular documents – “adaptive relevance
Inspired by e-commerce actors on the web, the delivered Enterprise Findability solution uses the logged behavior of its users to promote popular content. When an end-user searches for, e.g. “terawatt hours”, the solution by default offers search results ranked and sorted according to the generic relevance model. This is what any search tool would do. But this solution also uses search logs to promote popular content just as e-commerce sites have been doing for years – “Other people searching for ‘terawatt hours’ viewed ‘Current power production’ (intranet page), ‘Definition of terms in the electrical power industry’ (PDF document)” etc.
By combining the intel of the search logs (where the end-user behavior of an Enterprise Findability solution is constantly collected) and the best bets (editorially provided “sponsored links”) with the regular search result, end-users are presented with a rich set of information answering their original question from different angles. And the best part of it is that the social relevance feature constantly improves as the tool is used. People get better results as time goes by.
In addition to the social relevance feature, the vast amount of real search behavior compiled in the search logs is used for improving the generic relevance model as well. The solution tracks changes in popularity of content and adapts the document-level scores of documents and web pages in the search index accordingly. If a document is accessed often through the search tool, the document will be deemed “more important” and start climbing towards top positions in the search result. And if a previously popular document becomes less popular as time goes by, the document’s impact on the relevance model is decreased. In the end, content that has great importance for a limited amount of time (such as news items and weekly lunch menus) will first peek and then dip in the search index. The search index and the generic relevance model attached to it will stay fresh.
From generic to personalized search experience
This blog post has pinpointed a couple of solutions for a continuously-improving, generic relevance model in an Enterprise Findability solution. Obviously, generic models are generic, i.e. good enough for the many, not perfect for the few. There are great ways to address personalization solving many of the role-based challenges of Enterprise Findability, but let’s leave that to another, future blog post. Stay tuned!