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In the past year, at 904Labs A.I. for Search, we’ve put a lot of effort to optimize and push further the technology behind query intent/understanding engines. If you are in the search engine/information retrieval community, you know the term and how challenging the problem is. But if you are not familiar with the problem, it is hard to grasp its importance and challenges. Here’s my attempt to explain it in simple words.
A Query Intent/Understanding engine aims at identifying the intent of a user’s query and ultimately at translating it to a set of search directives.
Consider a user coming to your e-shop and typing in the search box: “red shoes”. The query may look pretty straightforward to you when it comes to what products to show to the user but, for a machine, it’s pretty hard to figure out what the person meant.
To get into the machine’s shoes (pun intended) think for a moment that you were born and raised in a warehouse, which is isolated from the rest of the world but has all the inventory of an e-shop. You’ve never left the warehouse so you don’t know anything about the world. It’s quite grim world but bare with me. One day someone slips a message under the door of the warehouse with the words “red shoes”. Now your task is to select a set of products that satisfy/cover that person’s information need. Obviously you know nothing about that mysterious person, let alone their preferences, and you barely understand the language of the message–perhaps you are able to recognize characters and words and match them to words found on the labels of products, but that’s pretty much it.
In such a surreal world, you can imagine that it is quite difficult, even for you, a human, to select a set of products that relate to “red shoes”. The challenge lies in that much of the important, contextual, information that we have access to from our constant interactions with the real world by living in it, it is very much missing in this artificial setting: We don’t know whether the user is a he or a she (and therefore we don’t know if we should pick male or female type of shoes), we don’t know what is the “hot” color of the season, nor the most popular brand, nor whether the person is looking for sneakers or boots or high-heels. There are lots of unknowns.
A Query Intent/Understanding engine is an algorithm that tries to make sense of the world for machines, or other entities, which are locked up in a similar warehouse as the one we described above and have no access to contextual information. A Query Intent/Understanding algorithm aims at mapping free text (a user query) to a series of directives (rules) that when applied, they will yield a useful set of products for the user. In our “red shoes” example, we are looking for directives that look as the following: “filter on products that have attribute:red and category:shoes”. At a first glance, the mapping looks simple but as of now you’ve seen from our warehouse setting that it can be quite daunting.
Researchers and practitioners have been working on this problem for quite some time and progress has been made; however the community has still some way to go before solving it. At the core of current solutions, there is a lot of complex technology such as neural networks that power NLP (Natural Language Processing) tools and other pipelines that require lots of human annotations (read expensive). These approaches yield relatively good accuracy, however, putting these systems into production may still be a challenging engineering problem–from setting up the data pipelining to scaling up, to retraining, and to monitoring system effectiveness and efficiency.
At 904Labs we’ve developed a Query Intent/Understanding engine that hooks up on an e-shop’s data, bootstraps a knowledge graph, and it learns the mapping to directives automatically, i.e., without supervision (read without human annotations). It doesn’t require retraining nor external dependencies, and you can use it straight away, today, on your own Apache Solr or Elasticsearch index (from which we only read and never write and we never lock you in). If that sounds appealing, get in touch for a one month free trial!
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As eCommerce is becoming increasingly important, we recruited a small team of researchers from major and not so major players in the field to share views and experiences on theoretical and practical challenges and future directions on eCommerce search and recommendations. The result of our effort, Challenges and Research Opportunities in eCommerce Search and Recommendations is published in June 2020 issue of SIGIR Forum.
This work is related to thoughts that I shared earlier on product search, and to 904Labs’ Query Intent engine and its magnificent results (examples).
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The vocabulary gap is still an open problem.
People refer to products in different ways that products are described in the catalogue. Neural networks, learning to rank, query intent engines are all important. Our experience at 904Labs shown that a query intent engine that boosts specific product categories given a query, boosts our learning to rank system by more than 16% in additional revenue. These results suggest that effective initial ranking is very important for effective learning to rank. To this end, we foresee an increasing interest in methods that can re-rank the entire collection and not only the top-N documents. This is particularly important for larger shops with large inventories (more than hundred of thousands of items) where a query can return hundreds of items, and only the top few are re-ranked.
E-commerce search is as much as about exploration as it is about finding the best match.
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On top of the engineering feat, the algorithm itself is pretty lean and has quite a few distinctive features that are hard to find in other engines: it does not depend on external dependencies (read Wikipedia), nor it needs periodic re-training. The only thing that a customer has to do is to plug in our system to a Apache Solr/Elasticsearch, and 904Labs Query Intent engine is built incrementally, one click/add-to-basket/purchase at a time. Oh, and it doesn’t write anything on your index. Magic!
Here’s a few interesting examples that came out from today’s experimentation:
(the examples are in Dutch; I try to translate the queries into English. Field attributes and values may be partially redacted for anonymity)
Some relatively simple cases
- The system learns to map the query:
lampen(lamps) to Solr’s directive:category:Lampen. Learning of this mapping is automatic and based on only from user interactions.
- The system learns to map the query:
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In our talk, I described the importance of search in e-commerce by giving examples of failed searches in a number of settings, from finding advertized items using onsite search to mobile search. I followed by with why people choose Amazon to start their product search and highlighted that 54% of them choose Amazon because of their great search functionality–that is the reason number five for people to go to Amazon. Then, I explained why optimizing the ranking manually is close to impossible for humans by laying out the insane amount of options available and enumerating the search space (which can be at millions of millions of choices). With this as foundation, I talked about machine learning and the particular type of machine learning that we use at 904Labs for optimizing search rankings in real-time. I followed up with describing our query intent engine, which is powered by 904Sense, and our automatic synonym extraction engine. In my conclusions, I re-iterated that A.I. for Search
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I moved to Barcelona in the first week of September for a three-month internship at Yahoo! Research Labs. I’m very excited about it, and I’m looking forward to getting to know the people here and the problems they are working on.
Update: My work at Yahoo! Research Labs resulted in a publication at SIGIR 2012 [1]; see [post] for the abstract(20120623-sigir2012-language-intent-models.md).
References
[1] Manos Tsagkias and Roi Blanco. 2012. Language intent models for inferring user browsing behavior. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘12). Association for Computing Machinery, New York, NY, USA, 335–344. ACM Link. PDF
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Starting my talk at ECIR 2018 Industry track. Courtesy of Gabriela Kazai. European Conference on Information Retrieval (ECIR) is a annual European scientific conference around bhe topics of search engines, recommender systems, text analytics, user modeling, and evaluation. This year ECIR was held in Grenoble, France. With more than 250 attendants and 4 days packed with tutorials, workshops, research, and industry talks, it was a great place to be to get updated with the latest and greatest about search engines.
The theme of this year’s Industry Day was to bring lessons learned from industry to academia. What are differences when developing a research algorithm and when we bring to practice? These lessons could inspire and inform our fellow academics for the challenges practitioners face when bring these algorithms to production.
