Search & Recommendation Engine Training

This task links clicks to carts—think “jeans” tied to a search or “liked” tagged in a buy (e.g., “trend” noted, “pass” flagged)—to train AI to guess what shoppers want next. Our team curates these patterns, boosting finds and sales with spot-on picks.

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the curation and structuring of data for Search & Recommendation Engine Training within retail AI workflows.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to label datasets that enhance AI’s ability to refine search results and product recommendations effectively.

Training and Onboarding

PMs design and implement training programs to ensure workers master preference tagging, search annotation, and pattern labeling. For example, they might train teams to tie “shoes” to a query or mark “frequent buy” in a history, guided by sample data and e-commerce standards. Onboarding includes hands-on tasks like annotating user journeys, feedback loops, and calibration sessions to align outputs with AI recommendation goals. PMs also establish workflows, such as multi-pass reviews for subtle preferences.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., curating 15,000 user interactions) and set metrics like search accuracy, recommendation relevance, or pattern consistency. They track progress via dashboards, address annotation errors, and refine methods based on worker insights or evolving shopping trends.

Collaboration with AI Teams

PMs connect annotators with machine learning engineers, translating technical requirements (e.g., high weight for recent buys) into actionable data tasks. They also manage timelines, ensuring curated datasets align with AI training and deployment schedules.

We Manage the Tasks Performed by Workers

The annotators, taggers, or data analysts perform the detailed work of labeling and structuring search and recommendation datasets for AI training. Their efforts are behavioral and analytical, requiring precision and retail insight.

Labeling and Tagging

For engine data, we might tag actions as “search” or “click.” In complex tasks, they label specifics like “price filter” or “cart add.”

Contextual Analysis

Our team decodes habits, tying “summer dress” to a hunt or marking “skip” on a pass, ensuring AI nails every shopper’s next move.

Flagging Violations

Workers review datasets, flagging mislabels (e.g., “viewed” as “bought”) or noisy data (e.g., bot clicks), maintaining dataset quality and reliability.

Edge Case Resolution

We tackle complex cases—like vague searches or rare tastes—often requiring deep review or escalation to retail experts.

We can quickly adapt to and operate within our clients’ e-commerce platforms, such as proprietary engine tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of interactions per shift, depending on the complexity of the data and annotations.

Data Volumes Needed to Improve AI

The volume of curated search data required to enhance AI systems varies based on the diversity of behaviors and the model’s complexity. General benchmarks provide a framework, tailored to specific needs:

Baseline Training

A functional engine model might require 5,000–20,000 annotated interactions per category (e.g., 20,000 search logs). For varied or niche patterns, this could rise to ensure coverage.

Iterative Refinement

To boost accuracy (e.g., from 85% to 95%), an additional 3,000–10,000 interactions per issue (e.g., missed recs) are often needed. For instance, refining a model might demand 5,000 new annotations.

Scale for Robustness

Large-scale applications (e.g., multi-market retail) require datasets in the hundreds of thousands to handle edge cases, rare searches, or new products. A curation effort might start with 100,000 interactions, expanding by 25,000 annually as systems scale.

Active Learning

Advanced systems use active learning, where AI flags tricky interactions for further curation. This reduces total volume but requires ongoing effort—perhaps 500–2,000 interactions weekly—to sustain quality.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and recommendation precision across datasets.

Multilingual & Multicultural Search & Recommendation Engine Training

We can assist you with search and recommendation engine training across diverse linguistic and cultural landscapes.

Our team is equipped to label and analyze search data from global retail markets, ensuring accurate, contextually relevant datasets tailored to your specific AI objectives.

We work in the following languages: