First up was Andrea Gazzarini presenting a brand new framework for search quality testing. Designed for offline measurement, Rated Ranking Evaluator is an open source Java library (although it can be used from other languages). It uses a heirarchical model to arrange queries into query groups (all queries in a query group should be producing the same results). Each test can run across a number of search engine configuration versions and outputs results in JSON format – but these can also be translated into Excel spreadsheets, PDFs or sent to a server that provides a live console showing how search quality is affected by a search engine configuration change. Although aimed at Elasticsearch and Solr, the platform is extensible to any underlying search engine. This is a very useful tool for search developers and joins Quepid and Searchhub’s recently released search analytics acquisition library in the ‘toolbox’ for relevance engineers. You can see Andrea’s slides here.

Martin White spoke next on how open source search solutions fare in corporate procurements for enterprise search. This was an engaging talk from Martin , showing the scale of the opportunities for open source platforms with budgets of several million pounds being common for enterprise search projects. However, as he mentioned it can be very difficult for procurement departments to get information from vendors and ‘the last thing you’ll know about a piece of enterprise software is how much it will cost’. He detailed how open source solutions often compare badly against closed source commercial offerings due to it being hard to see the ‘edges’ – e.g. what custom development will be necessary to fulfil enterprise requirements. Although the opportunities are clear, it seems open source based solutions still have a way to go to compete. You can read more from Martin on this subject in the recent free Search Insights report.

Thanks to Mimecast and both speakers – we’ll be back after the summer with another Meetup!

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The first talk was given by Karen Renshaw of Grainger, who Flax worked with at RS Components (she also wrote a great series of blog posts for us on improving relevancy). Karen’s talk drew on her long experience of managing search teams from a business standpoint – this wasn’t about technology but about combining processes, targets and objectives to improve search quality. She showed how to get started by examining customer feedback, known issues, competitors and benchmarks; how to understand and categorise query types; create a test plan within a cross-functional team and to plan for incremental change. Testing was covered including how to score search quality and how to examine the impact of search changes, with the message that “all aspects of search should work together to help customers through their journey”. She concluded with the clear point that there are no silver bullets, and that expectations must be managed during an ongoing, iterative process of improvement. This was a talk to set the scene for the day and containing lessons for every search manager (and a good few search technologists who often ignore the business factors!).

Next up were Christine Bellstedt & Jens Kürsten from Otto, Germany’s second biggest online retailer with over 850,000 search queries a day. Their talk focused on bringing together the users and business perspective to create a search quality testing cycle. They quoted Peter Freis’ graphic from his excellent talk at Haystack to illustrate how they created an offline system for experimentation with new ranking methods based on linear combinations of relevance scores from Solr, business performance indicators and product availability. They described how they learnt how hard it can be to select ranking features, create test query sets with suitable coverage and select appropriate metrics to measure. They also talked about how the experimentation cycle can be used to select ‘challengers’ to the current ‘champion’ ranking method, which can then be A/B tested online.

Pavel Penchev of SearchHub was next and presented their new search event collector library – a Javascript SDK which can be used to collect all kinds of metrics around user behaviour and submit them directly to a storage or analytics system (which could even be a search engine itself – e.g. Elasticsearch/Kibana). This is a very welcome development – only a couple of months ago at Haystack I heard several people bemoaning the lack of open source tools for collecting search analytics. We’ll certainly be trying out this open source library.

Andreas Brückner of e-commerce search vendor Fredhopper talked about the best way to optimise search quality in a business context. His ten headings included “build a dedicated search team” – although 14% of Fredhoppers own customers have no dedicated search staff – “build a measurement framework” – how else can you see how revenue might be improved? and “start with user needs, not features”. Much to agree with in this talk from someone with long experience of the sector from a vendor viewpoint.

Johannes Peter of MediaMarktSaturn described an implementation of a ‘semantic’ search platform which attempts to understand queries such as ‘MyMobile 7 without contract’, recognising this is a combination of a product name, a Boolean operator and an attribute. He described how an ontology (perhaps showing a family of available products and their variants) can be used in combination with various rules to create a more focused query e.g. “title:(“MyMobile7″) AND NOT (flag:contract)”. He also mentioned machine learning and term co-occurrence as useful methods but stressed that these experimental techniques should be treated with caution and one should ‘fail early’ if they are not producing useful results.

Ashraf Aaref & Felipe Besson described their journey using Learning to Rank to improve search at GetYourGuide, a marketplace for activities (e.g. tours and holidays). Using Elasticsearch and the LtR plugin recently released by our partners OpenSourceConnections they tried to improve the results for their ‘location pages’ (e.g. for Paris) but their first iteration actually gave worse results than the current system and was thus rejected by their QA process. They hope to repeat the process using what they have learned about how difficult it is to create good judgement data. This isn’t the first talk I’ve seen that honestly admits that ML approaches to improving search aren’t a magic silver bullet and the work itself is difficult and requires significant investment.

Duncan Blythe of Zalando gave what was the most forward-looking talk of the event, showing a pure Deep Learning approach to matching search queries to results – no query parsing, language analysis, ranking or anything, just a system that tries to learn what queries match which results for a product search. This reminded me of Doug & Tommaso’s talk at Buzzwords a couple of days before, using neural networks to learn the journey between query and document. Duncan did admit that this technique is computationally expensive and in no way ready for production, but it was exciting to hear about such cutting-edge (and well funded) research.

Doug Turnbull was the last speaker with a call to arms for more open source tooling, datasets and relevance judgements to be made available so we can all build better search technology. He gave a similar talk to keynote the Haystack event two months ago and you won’t be surprised to hear that I completely agree with his viewpoint – we all benefit from sharing information.

Unfortunately I had to leave MICES at this point and missed the more informal ‘bar camp’ event to follow, but I would like to thank all the hosts and organisers especially René Kriegler for such an interesting day. There seems to be a great community forming around e-commerce search which is highly encouraging – after all, this is one of the few sectors where one can draw a clear line between improving relevance and delivering more revenue.

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