Building Recommendation Systems: Collaborative Filtering to Deep Learning

Recommendation systems drive engagement and revenue across e-commerce, streaming, and content platforms. Whether you are building a product recommendation engine for a Bangladeshi e-commerce marketplace or a content feed for a media platform, understanding the spectrum of available techniques—from classical collaborative filtering to modern deep learning approaches—is essential for selecting the right architecture. Through our AI services, we have implemented recommendation pipelines that increased conversion rates by up to 34% for our clients.

Collaborative Filtering: The Foundation

Collaborative filtering operates on the premise that users who agreed in the past will agree in the future. User-based collaborative filtering finds similar users and recommends items they liked; item-based collaborative filtering finds similar items to those a user has already interacted with. While conceptually simple, these nearest-neighbor approaches suffer from scalability issues as the user-item interaction matrix grows sparse. Computing pairwise similarities across millions of users becomes prohibitively expensive without approximate nearest neighbor techniques like Annoy, FAISS, or ScaNN.

Matrix Factorization

Matrix factorization decomposes the sparse user-item interaction matrix into two low-rank matrices representing user and item latent factors. Singular value decomposition and alternating least squares are classic approaches, while Bayesian personalized ranking optimizes directly for ranking quality. The Netflix Prize popularized these methods, and they remain competitive baselines. Regularization, implicit feedback weighting, and temporal dynamics modeling all improve factorization quality. For many practical applications, a well-tuned matrix factorization model outperforms more complex methods while being easier to serve at low latency.

Neural Collaborative Filtering

Neural collaborative filtering replaces the linear dot product of latent factors with a neural network that learns non-linear user-item interactions. The architecture typically concatenates user and item embeddings and passes them through feed-forward layers. Deep and Wide models combine memorization of specific feature interactions with generalization from neural embeddings. AutoEncoder-based approaches like Variational Autoencoders for Collaborative Filtering learn robust latent representations from interaction histories and handle missing data gracefully.

Incorporating Side Information

Pure collaborative filtering ignores rich contextual signals: item descriptions, user demographics, temporal patterns, and device context. Hybrid models combine collaborative signals with content features. For an e-commerce platform, incorporating product category, price range, brand, and textual descriptions through a multi-tower architecture yields significantly better recommendations. Graph neural networks model the user-item interaction graph directly, propagating information through neighborhood aggregation to capture higher-order collaborative signals.

The Cold Start Problem

Cold start remains the most persistent challenge in recommendation systems. New users have no interaction history, and new items have no ratings. Content-based approaches address item cold start by recommending based on feature similarity. For user cold start, onboarding questionnaires, demographic-based initialization, and bandit-based exploration strategies help gather initial signals. In the Bangladeshi market, where new users may be unfamiliar with digital platforms, an intuitive onboarding flow that doubles as preference elicitation is critical for early engagement.

Evaluation and A/B Testing

Offline evaluation metrics—precision at K, recall at K, NDCG, and mean reciprocal rank—provide directional guidance but do not capture the full picture. Online A/B testing is indispensable for measuring real impact on business metrics: click-through rate, conversion rate, average session duration, and revenue per user. Be aware of feedback loops: a recommendation model trained on data it generated can amplify biases and reduce diversity. Inject exploration through epsilon-greedy strategies or Thompson sampling to maintain serendipity.

Building a production recommendation system requires orchestrating data pipelines, feature stores, model training infrastructure, and low-latency serving layers. Platforms like Bondorix provide the infrastructure backbone for deploying these systems. If you are looking to implement personalized recommendations, contact us for a consultation on architecture and strategy.