{"id":22798,"date":"2024-04-02T17:03:17","date_gmt":"2024-03-22T16:07:00","guid":{"rendered":"https:\/\/www.bloomreach.com\/library\/the-incredible-unlock-how-ais-revolutionizing-ecommerce-at-an-unprecedented-pace"},"modified":"2024-06-11T18:12:08","modified_gmt":"2024-06-11T18:12:08","slug":"how-ai-is-revolutionizing-ecommerce-at-an-unprecedented-pace","status":"publish","type":"library","link":"https:\/\/www.bloomreach.com\/en\/blog\/how-ai-is-revolutionizing-ecommerce-at-an-unprecedented-pace","title":{"rendered":"The Incredible Unlock: How AI\u2019s Revolutionizing Ecommerce at an Unprecedented Pace"},"content":{"rendered":"

Artificial intelligence, as we’re all aware, has permeated every facet of our lives. However, the sheer velocity at which AI has revolutionized our world is frequently underestimated. Take, for instance, the meteoric rise of ChatGPT. It astoundingly amassed 100 million monthly active users in a mere two months<\/u><\/a>. To put this into perspective, Facebook, one of the world’s most influential social media platforms, took four years to achieve the same feat.<\/p>\n

The momentum of AI is unrelenting, with projections indicating a staggering global economic contribution of $15.7 trillion by 2030<\/u><\/a>. Particularly in the realm of ecommerce, the phenomenal surge in AI advancements is propelling the entire sector into uncharted territories that were inconceivable just a few years prior. In this post, I will delve into the history of AI usage at Bloomreach and why the recent breakthroughs are so impactful for the ecommerce industry. <\/p>\n

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The \u201cTraditional\u201d AI Approach in Search<\/h2>\n

AI isn\u2019t new to ecommerce \u2014 for example, Bloomreach has been using AI<\/u><\/a> to power our first products. We started at first to leverage the power of natural language processing<\/u><\/a> algorithms to power our semantic search<\/u><\/a> capabilities. This improved the retrieval step of our system by increasing recall and precision. Utilizing ontology dictionaries, we were able to extract information from queries and products using algorithms like the Aho-Corasick search string algorithm. Then in the ranking step, we utilized the power of big data processing merging aggregate usage data from our customers with advanced ranking algorithms to deliver a much better consumer experience.<\/p>\n

Ultimately, the algorithms resolve into a set of if-then-else rules that runs well on a CPU. We would apply these heuristic algorithms as a way to streamline the amount of computing the AI needs to do. However, these algorithms have limitations. For example, when we see a word before another word in English, we generally know that the first word is the descriptor vs. the product, and can apply that heuristic to help the search engine parse queries and serve more relevant results. But this doesn\u2019t apply to every term, or in the case of languages outside of English, we would need different heuristics.<\/p>\n

\"Example<\/p>\n

To achieve both recall and precision, we needed extensive dictionaries that specified ontology and synonyms. However, these curated data sets that our algorithms used could lead to errors, as not all corner cases can be covered. For instance, we added the synonym \u201cOLED\u201d for \u201cTV\u201d because many people used the term OLED when looking for TVs. This worked for queries that were just \u201cOLED,\u201d but this simple algorithm would sometimes interpret a search for \u201cOLED TV\u201d as \u201cTV TV,\u201d which was in a lot of battery descriptions. As a result, without additional conditions to the algorithm to handle this particular case, it would yield irrelevant results.<\/p>\n

Building a generic algorithm that would apply to all corner cases becomes challenging, and thus a lot of exceptions would be needed. This ends up requiring too many conditional statements, making it too difficult for human programmers to build an algorithm to cover all the corner cases, let alone run it in a performant way on a CPU.<\/p>\n

This is where the recent advancements in AI come into the picture.<\/p>\n

The Fundamental Shift in AI <\/h2>\n

In recent years, generative AI<\/u><\/a> has brought about a seismic shift in our approach to AI. While many people now associate generative AI with ChatGPT, its applications extend far beyond content generation.<\/p>\n

The advent of deep learning utilizing neural networks<\/u><\/a> has been instrumental in the development of generative AI such as large language models<\/u><\/a> (LLMs). These new techniques apply deep learning methods, unlocked by the power of parallel computing on GPUs, allowing us to deal with complex understanding that scales by the amount of data that is fed to the models. An analogy is that instead of needing humans to create the perfect heuristic algorithm, we can now create a model-building algorithm that essentially creates a \u201cmuch better heuristic algorithm\u201d by training it with data\/examples. We call this model training, and the \u201cmuch better heuristic algorithm\u201d can be used to solve these natural language challenges quickly and cost-effectively through inferencing on a GPU.<\/p>\n

So, in the previous example, instead of needing a synonym and an algorithm to deal with inserting the right synonym into the correct terms, the term \u201cOLED TV\u201d is transformed through a language encoder model into a vector embedding. This embedding, a multi-dimensional set of numbers, represents the concept of OLED TV, which will be similar to a TV in terms of its Euclidean distance: no synonyms or dictionaries needed.<\/p>\n

\"Using<\/p>\n

We\u2019ve already been using the language model BERT<\/u><\/a> to help us scale our product discovery solution<\/u><\/a> into the French and German languages for parts of our algorithm like attribute extraction. These advancements are only the beginning of what\u2019s possible with AI as we begin to leverage the power of vector embeddings in the product domain, which will allow us to push the boundaries of our semantic capabilities.<\/p>\n

The Exceptional Pace of AI Innovation<\/h2>\n

ChatGPT seemingly exploded overnight, and that\u2019s honestly how it feels for generative AI technology as a whole. This is the fastest I\u2019ve ever seen technology innovation \u2014 in just a matter of months, we\u2019ve gone from a 200K context window to 10 million, which is two orders of magnitude! And improved models for every domain are now being released every week. This is an unprecedented pace of innovation, and while there are certainly challenges for us to navigate<\/u><\/a>, the technology also presents a wealth of opportunities. Just look at what we\u2019ve managed to achieve with conversational commerce<\/u><\/a>.<\/p>\n