Data Labeling Best Practices for Search Relevance 

Personalization is becoming a progressively important part of every business’s efforts to improve brand recognition. While personalization can be achieved through various strategies, search relevance is one of them.

Efficient search relevance is important because 68% of the shoppers will not return to a website when they have a poor search experience. Search relevance measures the closeness of the results after entering a query. While evaluating search relevance, data labeling helps build and train machine learning models and deliver accurate results. Search relevance is said to be relieved when the search results match user intent.

Let’s explore the data labeling practices to improve search relevance.

What is Data Labeling?

Data labeling includes building and training machine learning models for better search relevance evaluation. Using data labeling practices, we annotate and categorize data sets to improve the machine learning model to match search results relevance with a search query.

Following optimal data labeling practices, organizations can minimize the cost and time of labeling.

1.   Evaluator Training

Data annotators are tasked with repetitive and time-consuming operations. Given the sheer amount of data and scale of work, in-house teams working on data annotation face challenges.

In addition to the amount of data, a lack of domain knowledge can also lead to inaccurate data model training and categorization. Hence, pre-screening and qualification of human evaluators should be primary.

They need to work within strict guidelines, ensuring high-quality data training.

2.   Query Sampling

Query sampling for data labeling uses a representative data sample extracted from a larger data set. This sample data is used as the basis for labeling. This is done for two reasons;

• To check the accuracy of the algorithm in giving accurate results.
• To ensure that the representative data is correct for the larger dataset.

Query sampling helps minimize bias, leading to a correct evaluation of the data extracted from different sources. Statistical methods like random sampling are utilized in this approach.

3.   Data Labeling Project Design

Data labeling projects are time-consuming and complex, which is why having design workflows is essential to capture the essence of training data. Setting dedicated goals helps with careful planning and creates a work pipeline.

The motive here is to break down the humongous task of data labeling into smaller and simpler tasks. Within this, the data annotators, data labelers, and AI programs must have strict guidelines to follow.

4.   Extract Diversified Data

Ensure that you collect data from various sources, ensuring diversity. The data needs to capture information related to;

• Demographics
• Languages
• Geographic regions
• Age groups
• Search preferences

We can add more categories based on the type of data extracted and the potential outcome. For instance, training data for autonomous vehicles collected only during the day won’t suffice. Doing so, the AI model within the autonomous vehicle won’t detect objects during the night.

5.   Dataset Cleaning

Dataset cleaning includes fixing data removing incorrect, corrupted, and incorrectly formatted data. Because we gather data from multiple sources, data duplication and mislabeling can become a common problem. Incorrect data will lead to ineffective algorithms and unreliable results.

Hence, data cleaning is crucial. However, the method you choose for data cleaning depends on the type and scale of data plus the purpose of data collection. Here, you will need experts to fathom the scope of work progress and establish a data-cleaning template.

6.   Run Pilot Projects

Data labeling includes large-scale cleaning, annotation, categorization, and sorting, among other functions. Taking the plunge all at once is not ideal; hence you must begin with a pilot project.

As you test the waters, keep an eye on the results and the efficacy of the strategies you use to decipher the data and annotate. Pilot projects help determine the time required to complete the entire project, evaluate labelers’ performance, and quality assurance.

After the successful completion of the pilot projects, it will become easier to make changes and implement the same strategy on a larger scale.

Importance of Data Labeling for Search Relevance

Digital solutions and platforms we engage with every day become better with search relevance. As these platforms are connected with search engine, improving search relevance ensures that the search queries match the user’s search intent. Better search results will improve the user experience, and they are more likely to engage with your website or application.

Users usually check out the first 4 to 5 search results after entering a query and ignore the rest. With this approach in mind, it’s important to build a system wherein your products are showcased in the top search results on any platform. This is where data labeling can help.

Proper data labeling is also helpful in improving product development. As the data we collect is a hidden treasure highlighting potential customer’s preferences, feedback, and usage patterns, businesses can use this information to develop better products.

Today, human annotators are being assisted by artificial intelligence programs for quicker and more accurate data labeling.

Conclusion

Effective data labeling is a crucial part of improving search relevance. E-Commerce platforms and businesses benefit the most from data labeling as they need to bring up their products in search results, which leads to an increase in sales and revenue.

Data labeling leads to better decision-making and helps businesses understand real-world environments. At Shaip, we deliver content moderation services executed through data labeling and annotation to filter user-generated content and create a safe space for everyone using the web, especially with regards to your brand and business.

Author Bio

 Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.Linkedin: https://www.linkedin.com/in/vatsal-ghiya-4191855/