Embeddings and semantic search are essential concepts underpinning today’s advanced analytics and generative AI capabilities for digital marketers. This article explores the principles behind these technologies and their impact on modern digital marketing.
Take a look at the video to accompany this article, first with an overview of embeddings and semantic search:
Word Embeddings: Foundation of Semantic Analysis
In 2013, Google’s pioneering work on “Efficient Estimation of Word Representations in Vector Space” introduced the concept of embeddings. Embeddings map words into high-dimensional vector spaces, capturing their semantic relationships and probabilities based on their usage in large text corpora. Word embeddings are fundamental to semantic analysis, providing a powerful way to represent and understand the meaning of words in context. Here are some key concepts and benefits of word embeddings:
- Contextualized Word Representation: embeddings capture the meaning of words based on their usage in specific contexts. This allows for a more nuanced understanding of word semantics than traditional methods like one-hot encoding.
- Semantic Similarity: embeddings enable the calculation of semantic similarity between words. By measuring the distance between word vectors in the embedding space, we can determine how closely related two words are in terms of meaning.
- Predictive Power: word embeddings provide predictive capabilities. Given a word, embedding models can predict the words likely to appear before or after it in a sentence. This is useful for tasks like language modeling and machine translation.
- Efficient Computation: this makes them suitable for large-scale natural language processing applications. By reducing the dimensionality of word representations, embeddings enable faster processing and training times.
Applications in Semantic Analysis:
- Text Classification: Embeddings can classify text documents based on semantic content.
- Sentiment Analysis: Embeddings help determine the sentiment expressed in text, such as positive, negative, or neutral.
- Question Answering: Embeddings enable the development of question-answering systems that can extract answers from text based on their semantic understanding.
- Conversational AI: Embeddings power conversational AI systems by providing a deeper understanding of user queries and enabling more natural and contextually relevant responses.
Visual Embeddings:
Visual embeddings extend the concept of word embeddings to images and other visual data. Machine learning models can convert images into vectors that capture their semantic content and relationships by analyzing pixel densities and other visual features.
Visual embeddings play a crucial role in e-commerce product search. By converting product images into vectors, models can identify similar products based on their visual appearance, even if they are described using different words or phrases. This enhances the accuracy and relevance of product search results, creating a better experience by making it easier for shoppers to find what they want.
Predicting the best image to display on SERPS:
Adswerve teamed up with Google Cloud and Twiddy to put their new BigLake object tables to the test. This feature allowed us to image classifications and embeddings alongside Google Analytics SERP telemetry to predict the best possible image to feature. Doing this resulted in an 8–12% incremental improvement in vacation rental bookings.
Beyond these specific use cases, visual embeddings have a wide range of potential applications, including:
- Image Classification: Classifying images, such as animals, objects, or scenes.
- Object Detection: Identifying and localizing specific objects within images.
- Image Retrieval: Searching for similar images based on their visual content.
Applications in Digital Marketing
E-commerce Product Search: Embeddings can classify images of products based on similarity, making it easier for shoppers to find relevant items even when using ambiguous search terms.
Personalized Recommendations: Embeddings enable personalized recommendations by identifying products, movies, or articles that align with users' interests and preferences.
Dive deep into the topic and follow along, building a semantic search appliance using Cohere AI and then spinning up a managed service in Google Cloud Vertex AI Search and Conversation:
Conclusion
Embeddings and semantic search represent transformative technologies that have revolutionized digital marketing. By leveraging the semantic relationships between words and concepts, these technologies enable more accurate and relevant search experiences, enhancing customer engagement and conversion rates. As these technologies continue to evolve, they will continue to play an essential role in the future of digital marketing.