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Thoughts on the future of Search in E-commerce

I was invited to give a talk at the SIGIR Workshop On eCommerce 2019 but unfortunately, I am not attending SIGIR this year. Instead I wrote down some thoughts on interesting
problems, ideas and challenges in the e-commerce domain. Here they are:

  1. 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 (> hundred of thousands of items) where a query can return hundreds of items, and only the top few are re-ranked.
  2. E-commerce search is as much as about exploration as it is about finding the best match. From our experience at 904Labs, we see a large fraction of queries to revolve around categories, or combinations of categories, e.g., “red shoes”, “kitchen tables”, “dvd players”, “ebooks for 12 years old”. This type of queries go beyond our typical search and require understanding the query and generating a list of recommended relevant items. This proposition is supported by the surprising effectiveness of sorting by popularity; the most popular items for a query are potential good candidates for this “recommendation list” that the user is looking for. Back to query understanding of this type of exploratory queries, one would think that natural language processing can help here but in the e-commerce setting, queries are very short and any language analysis falls short. An open question here is how can we go from these broad queries to a good set of recommendations? A natural way is devise a hybrid system of search and recommendations: We fire the query to a search system and then we take the first few items as seed to a recommender system for getting similar items. Or a system that transforms a query to an image (think AttnGAN or similar) and each product to an image and then rank documents by their image similarity to the query’s–the image representation may constraint the latent space, abstract the language of the query and that of the document and be able to capture semantics that are otherwise difficult to encode in textual form. The image representation of queries and documents also offers explainability when it comes to explaining the rankings of the system; which is becoming increasingly quite important in machine learning-based systems.
  3. Evaluation metrics in e-commerce. There several directions here that we need more work. First, the e-commerce setting is a conjunction of exploratory search, typical search, and recommendations. Using the standard IR precision and recall measures for e-commerce may not tell us the entire story for how happy makes its users. We need to discover the aspects of a system that makes users happy for designing one or more metrics for evaluating search and recommendation systems. These metrics should also correlate well with revenue but also with customer loyalty (measured in returning customers and in shortening the time between returns). Such a metric (or a multiple of metrics) will then allow us to run offline experiments and make predictions on revenue, which is the main KPI that systems are evaluated in production for most e-commerce business.

Extra tip: we found that boolean scoring may be on par or outperform tf.idf or BM25 scoring in the e-commerce domain, it’s worth checking its effectiveness on your own data 😉

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A.I. Expo Europe 2018

This year we were invited to be part of A.I. Expo Europe, held in Amsterdam. The organizers were very kind to offer us a free booth in the Startup zone of the exhibition. The event took place on 27 and 28 June 2018, and it was packed with talks and visitors. There were three tracks: A.I., IoT, and Blockchain, with the latter two dominating the exhibitors list.

It was an intense two days, with lots of people dropping by 904Labs’ booth and asking about what we do and how 904Labs search technology can help them in their settings. Altough the average visitor profile was not looking for onsite search, we had good chats with a bunch of Dutch and international and small, medium, and big companies on how onsite search can help their endeavors. We talked on how search technology is underneath many applications, including chatbots, and self-service applications. No matter the application, good search can help increase revenue in the setting of selling products, or reduce costs in the setting of self-service as visitors are better able to find the information they’re looking for without calling the company’s call centre.

Besides core business, it was nice to see other startups and non-startups from Amsterdam and catch up with old faces and meet new ones. Overall, it was a fun experience. A big thanks to the A.I. Expo Europe organizers for inviting us over!

904Labs Query Intent Engine just got an upgrade and the results look great

Today at 904Labs Search we’ve been having fun with the next version of our Query Intent/Understanding engine.

If you don’t know what a Query Intent/Understanding engine is, here’s some background; otherwise keep reading.

Our Query Intent/Understanding engine is closing to one year old, and it’s pretty cool: it can quickly boostrap a knowledge graph and continuously update it straight from our customer’s data. Since its introduction in September 2017, it has been tested in many different domains and languages and has shown substantial revenue uplift across customers.

In the last couple of weeks, our team has been busy tweaking our engine and pushing it even further. Our Query Intent/Understanding engine was doing already a good job, mapping query free text to product attributes such as category, or brands. However, we’ve never tested going deeper into particular product attributes, such the product weight or even, wattage. Our engineers managed to extend the logic and the scalability of the algorithm to support very fine grained suggestions, if such fine-grained information exists in the index. It is quite a feat and I’m very proud of our team!

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)

First some relatively simple cases:

  • Query: “lampen” (lamps) translates into Solr’s directive: “category:Lampen”
  • Query: “Tent” => “category:Tenten”. Note how the system has learned the mapping from singular to plural without using of manual synonyms.
  • Query: “Stoel kussens” (chair pillows) => “category:Kussens” (pillows). Here the system mapped a more specific query to a broader category due to the taxonomy structure; there was no leaf in the taxonomy for chair pillows. After pushing all types of pillow higher, pillows for chairs are pushed further up downstream in the learning to rank process.

Some more difficult cases:

  • Query: “Bed 160×200” => “size:160×200 cm” and “size:160 x 200 cm”. Note how the system has learned the discrepency in attribute values and suggests both; in the first one there are no spaces in the representation of the dimension, in the second one there are.
  • Query: “stoelen” (chairs) => “category:… en eetkamerstoelen” (kitchens and dining chairs). Here the system attempts to infer the intented category for a broad query such as “chairs”. Although there is a category labeled “chairs”, the system decides to suggest a particular leaf within the taxonomy branch of chairs.
  • Query: “tuinset” (garden set) => “number_of_persons:12”. The system implicitly defines the size and dimensions of the garden set from historical user behavior. Given the lack of a user profile, suggestions like this aim at capturing the interests of the average user of the shop.

And finally, two of my favorites, where the system attempts to infer several attributes of products from broad queries:

  • Query: “prieel” (garden house) => “material:XXX, color:Beige, weight:4.9 kg”. Here the system maps a broad query to distinct set of attributes, i.e., the material, the color, and even the weight of the garden house. Again, with no user profile provided, these suggestions attempt to capture the interests of the average user of the shop.
  • Query “eenpersoons bed” (one person bed) => “size:90 x 200 cm”. The system maps the query to exact dimensions for the bed!
  • Query: “XXX lounge” (brand redacted) => “model:White XXX”. Here the system manages to map a brand and type of products from this brand to a specific product model from this brand. Again, without user profiling. Quite impressive!

I hope you also find exciting what the new version of 904Labs Query Intent/Understanding engine can do. You can have this technology working for you too and straight away, by getting in touch with us. Plus, unlike other search engine providers, you keep your index, and we don’t touch it so we don’t lock you in. The first month is for free, terminable monthly. Get in touch!

What is a Query Intent/Understanding engine?

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!

Talk at TECH Talks Amsterdam

On 31 May, I was invited to give a talk at TECH Talks in Amsterdam. TECH Talks is a new meetup organized by Techloop.io, a new way for matching jobs and talent in IT. My talk revolved on how we built an e-commerce focused search engine using machine learning (or as most people know it, A.I.). The meetup had a great start with more than 200 registered people and more than 100 people showed up; the room at TQ was at its maximum capacity! The audience was diverse with a nice mix of frontend, backend , senior and junior engineers, and also people from other disciplines who are interested in keeping up with latest developments in IT and e-commerce. Techloop and TQ were great organizers providing a great atmosphere (pizza and beer, included) facilitating great chats and networks afterwards.

You can see the video of the talks here (mine is the first after the introduction by Techloop):

 

The slides of my talk are here:

Talk at ECIR 2018 Industry day

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.

In my talk I touched upon a bunch of challenges we’ve faced at 904Labs and how we came about to solving them. Open questions revolve around online learning to rank algorithms, delayed feedback, design of new metrics to avoid embarassing results, and the importance of investing in an evaluation platform. At 904Labs we have come a long way and we have working answers for these questions, however, as these questions are particularly difficult to answer, I’ve invited people to drop me a note with ideas, if they are interested. I’ve already got some nice feedback, and I hope to see more research on these areas in the near future!

Below are some tweets from my talk.

Talk at Frankfurt Data Science meetup

I was happy to be invited at Frankfurt Data Science meetup to talk about data science, meetups, and how to build a data science-oriented startup. The event was held at Frankfurt School on March 1st, 2018. In my talk I gave an overview of the meetup scene in Amsterdam, briefly presented Amsterdam Data Science and its activites, and then I shared my experiences on founding and developing 904Labs, before I delved into one of my favorite topics: machine learning and search.

It was a packed room, with more than 200 registered people for the event, and the talk was broadcasted live on YouTube. There will soon be a video, which I will share here. The audience was from diverse backgrounds, and I enjoyed the interactions very much. I was happily surprised by the professional organization of the event, and the ambition for making Frankfurt one of the leading centres in data science in Europe. All the best to the organizers and I hope to see more collaboration on data science between Frankfurt and Amsterdam in the near future!

The talk is online on YouTube (thank you Frankfurt Data Science for making this happen!):

Talk at A.I. for Commerce meetup

Emakina, one of the largest web agencies in Europe, works with internationally acclaimed brands on their branding and electronic presence. To keep their customers ahead of the curve, Emakina has recently started a series of meetups where experts in a wide range of fields come and talk about the latest developments in their field. The last meetup was held last Thursday, 8 February 2018, and with the topic: “A.I. for Commerce”, three talks were scheduled: one from Emakina, one from 904Labs, and one from Salesforce.

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 can boost search-driven revenue by 30% and that search is becoming part of platforms. In this angle, it is important for online retailers to test the claims of their vendors by doing A/B tests before they opt in for a solution.

Amsterdam City A.I. Event

I was happy to be invited at the Amsterdam City A.I. Event on December 11, 2017. My talk revolved around our experiences at 904Labs in building a A.I. focused company. It was fun to be among A.I. enthusiasts and to see that people identified with our experiences–which means that we are on good track!

The talk is online on YouTube (clicking the link will start at my talk):

Below are some tweets from the event:

Another historical moment for 904Labs

I’m very happy to share news on the latest success of 904Labs A.I. for Search with our new customer, eci.nl.

eci is one of the largest online book specialists in the Netherlands and Belgium, with an estimated yearly revenue of 15m euro for the Dutch part. For their site search, eci relied on a manually optimized Apache Solr, integrated into Intershop.

During a three-week A/B test we’ve shown a 38% improvement in revenue and a 34% increase in conversion rate compared to the in-house Apache Solr search engine. This shows once more that adding A.I. to your search engine really makes a difference! Read the full story here.