eCommerce Search and Product Discovery AI

Data Gathering & Engineering

• We collated and cleaned website content, including product catalogs, articles, manuals, and metadata, making it ready for indexing and search processing.
• Search logs, user queries, and click-through data were collected to provide insights into actual user behavior.

Text Annotation & Data Structuring

Using NLTK (Natural Language Toolkit), we annotated the data, tagging words and tokens with parts of speech and semantic roles, helping the AI understand relationships and meanings between terms.

Exploratory Data Analysis (EDA)

We analyzed query patterns and relationships in the search logs, visualizing frequency distributions of standard terms. This process helped identify potential data quality issues, ensuring the data was clean and relevant for the AI model.

Text Vectorization

Using TF-IDF, Word2Vec, and Bag of Words (BOW) techniques, we transformed the processed text into numerical vectors. We used cosine similarity to assess the relevance of search results, ensuring the most relevant products were returned for user queries.

AI Model Training

We trained the AI search model using a Transformer-based neural network and fine-tuned it with domain-specific data to adapt to the client’s catalog. The model was evaluated based on recall, precision, and relevance, and we iterated on the architecture and hyperparameters to optimize performance.

Developing the Search Interface

• We designed a user-friendly, conversational search UI with auto-complete and search suggestions to guide users to relevant results.
• We configured this search interface to support intent detection and spelling correction, improving search accuracy and reducing user frustration.
• Transparency was maintained by providing explanations of how search results were ranked.