We saw that our previous article on large language models (LLMs) for publishers has been well received. Thanks to that enthusiasm, we will be writing a series of articles on different aspects of AI technologies, with a focus on their benefits for publishers.

In this article, we will discuss different aspects of AI in search services on media websites.

A Brief History of AI in Search

While LLMs and GPTs have gained significant attention recently, AI techniques have been around for over a quarter of a century. Many websites already utilize AI through Statistical Natural Language Processing (NLP)- based systems like Elasticsearch, Solr, or similar technologies. These systems are robust and can efficiently index and search vast amounts of data. So, congratulations, if you are using these technologies, you are already leveraging AI!

However, the AI landscape is constantly evolving, and new advancements are making it possible to enhance search capabilities even further. These newer AI tools can fundamentally transform the search experience on your websites.

Improving Search Fidelity with Neural Search

Neural search represents a significant forward leap in search technology. While the concept has existed for some time, it was not widely adopted due to high implementation and computational costs. Today, advancements in AI have made neural search more accessible and practical.

Neural search excels at understanding context, handling synonyms, and managing related concepts automatically. This results in a more accurate and relevant search experience for users. It can also handle ambiguity better, ensuring that users find what they are looking for even if they use different terms or phrases.

Uncovering Patterns with AI Clustering

Traditional search approaches often struggle to identify patterns in search queries. Newer AI techniques, however, enable easy clustering and pattern recognition across search queries. This capability provides publishers with valuable insights into user content expectations.

AI can identify trends and preferences by analyzing search queries. With an enhanced understanding of content preferences, publishers can more effectively align and tailor their content for better user engagement.

Integrating Multimedia into Search

One of the most exciting advancements in AI search technology is the ability to include images, audio, and video content in the same search index as textual content. This coherence opens up new dimensions in content discoverability.

Imagine a user searching for an article on a particular topic and finding not only text, but also relevant images, videos, and podcasts. This multi-modal search elevates user experience to a much higher level leading to a "wow" experience.

Embracing Question and Answer Systems

Another innovative approach to search is the question-and-answer system. Users can interact naturally in a conversational way, asking questions instead of typing keywords into a search box and receive direct answers.

This method of interaction is intuitive and user-friendly. It aligns with how people naturally seek information, making the search process more convenient and efficient. To increase trust, the newer AI techniques can add references to the material it used to derive the answer.

Implementing a question-and-answer system can significantly enhance user engagement and satisfaction for publishers.

Conclusion

The evolution of AI is opening up exciting possibilities for digital publishers. By leveraging modern AI tools, publishers can transform their search capabilities, providing users a more accurate, relevant, and engaging experience. From neural search and pattern recognition to multi-modal search and question-and-answer systems, these advancements are game-changing on how users discover content.

At Kreatio, we are committed to enabling our clients harness the power of these AI innovations. Stay tuned for more articles in our series as we continue to explore how AI can help publishers thrive in the digital age. 

Deepak Kumar

Author

Deepak has a B.Tech Computer Science and Engineering degree from IIT Kanpur. He has worked in ML (Machine Learning) and AI for over 25 years and is instrumental in building transformative AI systems.