Training data drives machine learning, and machine learning promotes rich human-computer interaction experiences. In the Internet era of rapid iteration, we are constantly bombarded with various advertisements, and we often even think about it with horror, "Oh my god, how did Xiaohongshu know that I ran out of facial mask." This is all due to the uncanny insights of algorithms and machine learning into users' Search intent.
Search relevance optimization through machine learning technology
What results do you expect when you enter a query into your favorite online retailer search bar? Today, most customers expect search engines to deliver data that is highly accurate, relevant, and instant. Search engines should provide a personalized experience and anticipate your needs—even if your wording or spelling is a little off or your query is a little vague. Several companies are turning to artificial intelligence (AI) to power their own search engines and excel in these areas. Optimizing search relevancy (i.e., the accuracy between a query and a search result) is critical for many large businesses with strong websites, especially online retailers. More than 40% of customers go directly to the search bar first, which means the search bar is their first impression of the website. Search engines direct customers to the products and information they want, ideally leading to sales. An engine with high search relevance can improve customer satisfaction, conversions, and retention, while a poor search experience can disappoint and negatively impact customers. While most companies understand that integrated search is an integral part of the customer experience, how does search relevancy come into play? Several companies are deeply researching and investing in using machine learning (ML) technology to improve search relevancy. These businesses use customer behavior and analytics techniques in search relevancy machine learning programs to connect customers with what they want.
AI-powered search vs. basic search
Search keeps pace with the times. In the past, search engines typically quantified the number of times a search keyword appeared on a web page; the higher the number, the higher a specific thing (website, product, etc.) ranked in the search results. This basic search method (matching query content to text in documents) is still used on many websites, especially those of smaller companies. But in 1998, Google changed all that. Google entered the field and became the first company to use advanced statistical analysis techniques to interpret and classify queries. Today, many companies use statistical analysis powered by AI to drive search results. This is partly due to the increased complexity of the results. Content structures have evolved to have not only text, but also tags, descriptions, category tags, and other searchable metadata. Additionally, companies now want to combine their business priorities, a user’s geographic location, the user’s past behavior, and other contextual factors to determine the specific relevance of content for each individual. These complicating factors lead to the need for sophisticated algorithms to arrive at interpretations and output solutions. AI is able to further distinguish low-quality content from high-quality content and rank them accordingly. For example, AI can identify search engine optimization (SEO) techniques that try to unfairly benefit from algorithms (such as stuffing keywords and invisible text into product descriptions or web pages) and place those search results in a high-quality , below intent-driven results.
The challenge of improving relevance
Customers have never been more demanding; we have high expectations for a tailored experience and easy access to what they want. However, there are big differences between people. I might type "double bedding" and you might type "quilt" into a search query, but it's very likely that both of us will end up searching for the same product. People will ask the same problem in different ways; learning all these possible iterations is difficult for any algorithm. However, before understanding iteration, the algorithm must also have a basic understanding of our language. Natural language understanding is the discipline that allows machines to learn human language. To successfully achieve search relevance, the model must be able to detect the meaning of a word (e.g. "bedding") and provide appropriate results. To further complicate the situation, when I type "beding", the search engine is also supposed to guess that I actually mean "bedding". The model must account for spelling, typos, and grammatical errors because these situations are common. Training search engines to understand our natural language requires large amounts oftraining data. Given the expense, time and effort required to collect and prepare such data, many companies, especially small and medium-sized ones, may find this an insurmountable obstacle. However, these companies should not be discouraged. Learn how Shotzr worked with Appen to identify over 17,000 images that did not require additional tags so they could focus on those that did, improving the search relevance of stock photos.
ML-based search relevance optimization method
AI-powered search engines rely onnatural language processing (NLP) to read, understand, interpret and analyze queries. As mentioned earlier, these models designed to improve search relevance rely on natural language data for training. This data must cover millions of use cases and edge use cases, from vague to precise. A good algorithm should provide relevant search results even when the query is not easy to understand. There are many techniques in the natural language processing discipline, including semantic annotation, text analysis, and named entity recognition. Our introduction to NLP covers a lot of this. What is important to understand is that these technologies equip machines with the tools to parse text and reveal its meaning. Search engines can use derived meaning to detect the results of a query and improve its ranking. Search relevance models can also use click tracking, which uses an individual's past queries to determine which results would also be statistically appropriate for that person. Certain search engines, such as Google Image Search or Adobe Stock Photos, require image analysis. Like NLP, image analysis techniques require large amounts of high-quality, annotated image data. Image analysis helps machines classify images and image quality into relevant, searchable features. When using any of the above ML technologies, it is recommended to use a human-machine collaborative approach to provide live monitoring. For example, a person can evaluate whether a particular query provides relevant search results. In the event of an error, humans can provide feedback to the machine to improve its accuracy. As query input evolves, machines must adapt. Traditional queries were text-based, but now we're seeing opportunities to search using images or query using voice. These opportunities will add new layers of complexity to search engines, but are not insurmountable.
Insights from Appen Search Relevance Expert Kelly Sinclair
Appen relies on its own team of experts to help you build cutting-edge models to successfully achieve search relevance with AI-driven search. Then you'll be able to deliver a great customer experience and increase your business ROI. Our Director of Client Services, Kelly Sinclair, is one of our team's leading experts on ensuring clients' success in achieving and improving search relevancy through machine learning. Kelly’s top three insights for successful search relevancy programs include:
- Identify business needs. Relevance is challenging. It may be highly dependent on many changing variables, such as semantics, location, or context. User intent is crucial because this type of work can be subjective. A query a user makes on a mobile device will get completely different results than if they perform the same search on a desktop computer. Success comes from a deep understanding of each project and its goals. These goals should be specific, measurable, achievable and relevant
- Establish clear goals and metrics for the project. Developing high-quality data does not happen overnight, but requires training, reinforcement, and expertise built over time. To do this, we must define success. These measurable outcomes should be embraced and agreed upon by all stakeholders involved. Projects are dynamic, and as each cycle iteration brings improvements in efficiency and data quality, we should examine metrics to ensure we are still delivering value.
- Implement data-driven decisions. The data-driven decision-making process begins with data collection based on measurable goals and identifying data signals. Machine learning helps identify gaps, recognize patterns in decision-making, and areas for improvement. We can then employ an analytical approach to determine next steps in response to these insights.