Why Your Business Needs a Recommendation Engine: Inside Our Customizable Two-Tower Model

Modern consumers are bombarded with choices. On an e-commerce site there might be hundreds of similar desks, shoes or playlists to sift through. This "choice overload" can lead to buyer paralysis where shoppers abandon their carts because they can’t easily find what they need. Recommendation engines fix this by surfacing the most relevant items. In fact, recommendations account for a huge share of user activity on the world’s biggest platforms - roughly 70 % of what people watch on YouTube, 35 % of Amazon purchases and 80 % of Netflix views come from recommender systems. These numbers demonstrate how effective personalisation can be at guiding users through vast catalogs.

It’s not just big tech that benefits. A quality recommendation engine can:

Drive traffic, increase engagement and convert browsing into buying. During Black Friday 2021, Gymshark saw a 150 % increase in order rate and a 32 % uplift in add-to-cart rate after adopting a recommendation system.
Increase cart value by presenting related or frequently-bought-together items and shorten the path to purchase with targeted content that matches a shopper’s intent.
Improve customer satisfaction and retention: 56 % of customers are more likely to return to an e-commerce site that offers recommendations, while 74 % feel frustrated when content isn’t personalized.

Why Businesses Invest in Recommendation Engines

Behind these headline numbers lie several concrete business benefits:

Boost revenue by surfacing cross-sell and upsell opportunities; showing shoppers items that complement their interests encourages additional purchases and increases order values.
Enhance customer satisfaction and retention because suggestions feel tailored to individual preferences.
Build individual user profiles from behaviour data and deliver personalisation at scale, raising conversion rates.
Generate analytical reports that reveal customer behaviour, conversion patterns and engagement trends, enabling data-driven decisions.
Reduce manual workload by eliminating the need for staff to curate lists.

The economic case is compelling: according to a recent survey, over 75 % of businesses report positive ROI from personalisation initiatives, and product recommendations account for roughly 31 % of an online store’s revenue. When customers feel understood and are presented with timely, relevant items, they’re more likely to buy, return and advocate for your brand.

How Recommendation Engines Work

At a high level, a recommendation engine takes signals about users and items and predicts which items a user is likely to find appealing. Traditional approaches include collaborative filtering, which leverages the behaviour of similar users, and content-based filtering, which compares item attributes. Modern systems often combine multiple methods and employ deep learning to learn richer representations.

One of the most widely used deep-learning architectures is the two-tower model. In a two-tower system, a user tower processes user-related features - such as past interactions, demographic information or device context - into a dense user embedding. A separate item tower processes item features - such as IDs, categories, descriptions or images - into an item embedding. Instead of feeding all features into one large network, the model scores user-item pairs by computing a simple similarity (often the dot product) between their embeddings. Because item embeddings depend only on item attributes, they can be pre-computed offline; when a user request arrives, the system calculates the user embedding on the fly and then performs an approximate nearest-neighbour search over the item embeddings to find the top candidates. This decoupling makes it feasible to retrieve relevant items from catalogs containing millions or even billions of products in milliseconds.

NeuronSearchLab’s Customisable Two-Tower Engine

At NeuronSearchLab, we’ve built a recommendation engine around this two-tower architecture but added flexibility where it matters most: the events that define your user’s journey and the scores associated with those events. Instead of relying on a fixed set of signals (views, clicks, purchases), our platform lets you define your own business-specific events. You might track video watch time, article reads, wishlist additions, subscription renewals or even offline actions such as in-store visits. You can assign each event a weight to reflect its importance to your objectives - for example, giving purchases a higher score than page views, or amplifying new-product interactions during a launch campaign. By tailoring event scores, you align the engine’s optimisation goals with your key performance indicators.

Under the hood, these events feed into the user tower as features. When a user triggers an event, we update their embedding with the corresponding weighted signal. Over time, the embedding captures both long-term preferences and recent signals, enabling the engine to react to evolving tastes. The item tower works similarly, incorporating your catalogue metadata along with any custom attributes you choose to include. Because both towers produce embeddings in the same vector space, you get fast retrieval and ranking using approximate nearest-neighbour search - no matter how many items or users you have.

Another differentiator of our engine is its adaptive training schedule. We retrain the user tower regularly, expanding the training set as more data arrives. This allows the model to start with a narrower "net" when data is scarce and gradually widen its understanding as engagement grows. As your business scales and customer behaviour diversifies, the engine becomes more accurate. Retraining also helps combat concept drift - when user interests change over time - so your recommendations stay fresh. Because item embeddings are precomputed, retraining doesn’t disrupt your production pipeline; new item vectors can be generated offline and uploaded seamlessly.

Why Choose Our Solution?

Align with your goals: Custom event definitions and scores let you optimise for what matters - be it conversions, time-on-site, subscription renewals or content diversity. You’re not constrained to a generic set of signals.
Scalable and efficient: The two-tower architecture with offline item embeddings scales to millions of users and items while delivering real-time responses.
Continuous learning: The adaptive training schedule keeps the model current as behaviour patterns change. You don’t need a large dataset to start; the net widens automatically as data grows.
Data-driven insights: Built-in analytics dashboards expose the rich customer data flowing through the engine, helping you understand engagement and inform merchandising decisions.
Reduced engineering burden: We host the infrastructure and handle the complexities of pre-computing embeddings, approximate nearest-neighbour search and model retraining. Your team focuses on crafting great experiences rather than maintaining recommender infrastructure.
Proven impact: Recommendation engines demonstrably improve sales, engagement and retention across industries. With NeuronSearchLab you gain these benefits without building a system from scratch.

Putting It All Together

Whether you run an e-commerce shop, a media platform or a B2B marketplace, personalised recommendations can transform the way customers discover your content. They streamline decision-making, surface hidden gems and encourage deeper engagement. Our two-tower architecture gives you the performance and scalability proven by industry leaders, while the ability to define and weight your own events ensures that recommendations reflect your unique business logic.

When customers feel understood, they buy more, stay longer and come back often. And when you can transparently link recommendation strategy to revenue and engagement metrics, it becomes easier to justify investment and plan growth. Let NeuronSearchLab be your partner in building smarter, more human-centric experiences.

Frequently Asked Questions

What kinds of events can I track? ⌄

You can track any user action that’s meaningful to your business: product views, video plays, add-to-cart actions, purchases, form submissions, content reads, offline purchases and more. Each event can be assigned a weight to reflect its importance.

How often does the model retrain? ⌄

We retrain the user tower on a recurring schedule - initially more frequently when data is sparse and then at regular intervals as your dataset grows. Item embeddings can be updated offline whenever you introduce new products or metadata.

Do I need machine-learning expertise to use NeuronSearchLab? ⌄

No. Our platform abstracts away the ML complexity. You define events and their weights via a simple interface. We handle model training, approximate nearest-neighbour search and serving, and provide analytics to monitor performance.

Can recommendation engines really increase revenue? ⌄

Yes. Studies show that recommender systems drive cross-sell and upsell opportunities, increase average order value, and improve overall conversion rates. They also encourage repeat visits.

What makes the two-tower model scalable? ⌄

By decoupling user and item computations, the two-tower architecture allows item embeddings to be pre-computed offline and stored. At serve time the system only needs to compute a single user embedding and run an approximate nearest-neighbour search over the item embeddings, enabling real-time recommendations even with massive catalogues.