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Recommendation Systems

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Key Capabilities of Our Recommendation System Solutions

Collaborative Filtering (User-Item Behavior Modeling)

Collaborative filtering is one of the most widely used techniques in recommendation systems. It predicts user preferences based on historical interactions such as clicks, ratings, purchases, or likes. We use:

we use include:

User-based Filtering –

Finds similar users and recommends items they’ve liked.
Item-based Filtering –

Recommends similar items based on what a user has engaged with.
Matrix Factorization –

techniques (e.g., ALS, SVD, NMF) to reduce

Recommending products to a user by analyzing what similar users have bought or rated highly.  

Content-Based Filtering (Using Item or User Metadata)

Content-based recommenders focus on the attributes of items and user profiles to make predictions. They use structured metadata such as:

Item tags –

genre, brand, price range
User demographics

location, age, preferences
Textual content analysis –

using TF-IDF, BERT, or Doc2Vec

These models compute similarity using cosine similarity, Euclidean distance, or neural embeddings.

Suggesting movies of the same genre or from the same director that the user has previously liked.

Hybrid Recommendation Models

Hybrid systems combine collaborative and content-based approaches to overcome limitations such as the cold start problem, sparsity, and popularity bias. Techniques

we use include:

Weighted blending of CF and CBF scores
Meta-level models using decision trees or gradient boosting
Deep hybrid models combining DNNs with matrix factorization layers

Improving recommendation accuracy for new users or items by combining user preferences and item features.  

Session-Based Recommendations Using Real-Time Clickstream Analysis

In many modern applications, especially in eCommerce and media, user sessions are short and context-rich.

We build models that:

Capture behavior within a single session
Use Recurrent Neural Networks (RNNs) and Transformer architectures
Incorporate attention mechanisms to focus on critical user actions
Leverage real-time streaming data through Apache Kafka and Flink

Use Cases

Suggesting the next product or content item based on the user’s current session activity.

Contextual Recommendations (Time, Location, Device, Intent)

Context-aware recommendation systems leverage dynamic and environmental variables to improve relevance.

These include:

Temporal data –

time of day, day of week, season
Location data –

GPS, geofencing
Device type –

mobile, desktop, smart TV
Intent signals –

derived from NLP and session flow

We build models using contextual bandits, multi-armed bandits, and contextual embeddings.

Use Cases

Recommending a lunch meal around noon from nearby restaurants using mobile location.

Deep Learning-Based Recommenders (DNNs, Transformers, Autoencoders)

We apply modern deep learning to model complex, nonlinear relationships between users and items. Some architectures

We use include:

Autoencoders –

for dimensionality reduction and noise filtering
Multilayer Perceptrons (MLPs) –

for nonlinear interaction modeling
Transformers –

for long-term behavior modeling with attention
Two-Tower Models –

separating user and item encoders for scalable matching
NLP-based models –

like BERT4Rec for sequence-aware recommendation

Use Cases

Delivering highly personalized feeds in media apps by capturing user tastes and session context.

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Industry Use Cases of Recommendation Systems

Our recommendation engines are deployed across diverse industries to increase customer engagement, lifetime value, and conversion rates.

eCommerce

Personalized product recommendations based on browsing and purchase behavior
Cross-sell and upsell engines to increase average order value
Dynamic bundling and promotion systems
Frequently bought together suggestions using association rule mining

Tech Stack:

TensorFlow Recommenders, Faiss, Neo4j, Spark MLlib

Media & OTT

Tailored content feeds using session-based and collaborative filtering
Continuation and binge-watch detection
Multi-modal recommender systems (video + text + metadata)
Trending and genre-based personalized curation

Tech Stack:

Transformers (BERT4Rec), RNNs, Attention Models, Weaviate, Pinecone

EdTech

Learning path recommendations based on skill level and previous courses
Personalized assessments and practice suggestions
Engagement-based recommendations (e.g., quizzes, video lectures)
Adaptive testing systems

Tech Stack:

DNNs, Bayesian Knowledge Tracing, Content Tagging APIs, LangChain

Finance

Portfolio recommendation based on risk appetite and financial goals
Transaction tagging and merchant suggestions
Credit card reward optimization
Dynamic pricing and personalized financial advice

Tech Stack:

LightGBM, Scikit-learn, Custom MLOps Pipelines, Vector Databases

Retail

In-store product recommendations via mobile apps
Planogram optimization based on behavioral data
Loyalty-based suggestions and dynamic offers
Context-aware promotions based on weather, events, or location

Tech Stack:

Hybrid recommenders, Real-time analytics with Kafka, Contextual Bandits, Autoencoders

Technologies We Use for Recommendation Systems, Anomaly & Fraud Detection, and Time Series Forecasting

At Tecorb Technologies, we employ a robust set of tools, libraries, and frameworks to build intelligent systems that personalize user experiences, detect anomalies in real time, and forecast future trends with precision. Here’s a breakdown of the technologies we leverage across these advanced AI/ML domains:

Machine Learning & Deep Learning

Frameworks

TensorFlow / PyTorch -

Core ML/DL frameworks for model building and experimentation
XGBoost / LightGBM -

High-performance gradient boosting for structured anomaly detection and forecasting
Scikit-learn -

Traditional ML algorithms (SVMs, decision trees, isolation forests, etc.)
Keras -

Simplified interface for deep learning models

Recommendation Systems

TensorFlow Recommenders (TFRS) -

Deep learning models for collaborative filtering and ranking
LightFM -

Hybrid recommendation models (matrix factorization + metadata)
Implicit -

Alternating Least Squares (ALS) for large-scale implicit feedback
Faiss / Annoy / ScaNN -

Efficient approximate nearest neighbor search for similarity-based recommendations
Vowpal Wabbit -

For large-scale online learning and personalization

Anomaly & Fraud Detection

Isolation Forests -

Tree-based unsupervised anomaly detection
One-Class SVMs -

For high-dimensional anomaly boundary detection
Autoencoders -

Neural architectures to reconstruct normal patterns and flag outliers
LSTM / GRU Networks-

For sequence-aware fraud detection in transactional systems
Prophet + Custom Rules Engines -

For hybrid statistical + rule-based anomaly detection
ELK Stack (Elasticsearch, Logstash, Kibana)-

For real-time log anomaly detection pipelines
Kafka + Spark Streaming -

For processing and detecting anomalies in real-time data streams

Time Series Forecasting

Facebook Prophet -

Quick and reliable forecasting with holidays and seasonality support
ARIMA / SARIMA / VAR -

Traditional statistical models for univariate and multivariate forecasting
GluonTS (Amazon) -

Deep learning for probabilistic time series forecasting
NeuralProphet -

Combines deep learning and Prophet features
Darts (by Unit8) -

Unified framework supporting ARIMA, RNN, Transformer-based time series models
DeepAR (AWS) -

Probabilistic forecasting for demand, finance, and capacity planning
N-Beats / Informer / Temporal Fusion Transformer -

State-of-the-art deep architectures for multivariate forecasting

Supporting Technologies & Toolkits

Pandas, NumPy, Dask -

Data wrangling at scale
MLflow / Weights & Biases -

Model experimentation and versioning
Airflow / Kubeflow -

ML pipeline orchestration
Docker + Kubernetes-

Scalable deployment of ML inference services
AWS SageMaker / Azure ML / GCP Vertex AI -

Managed platforms for training and deployment

Real results for real business

Empower your operations with human-like AI agents, seamless integrations, and intelligent workflows for unmatched efficiency.

Dating Application

Achieved 4x efficiency with automated appointment scheduling and follow-ups.

Education Business

Increased lead conversions by 5x using personalized AI interactions.

Recommendation Systems

Key Capabilities of Our Recommendation System Solutions

we use include:

we use include:

We build models that:

Use Cases

These include:

Use Cases

We use include:

Use Cases

Try advanced fraud prevention technologies to safeguard your assets.

Industry Use Cases of Recommendation Systems

eCommerce

Tech Stack:

Media & OTT

Tech Stack:

EdTech

Tech Stack:

Finance

Tech Stack:

Retail

Tech Stack:

Technologies We Use for Recommendation Systems, Anomaly & Fraud Detection, and Time Series Forecasting

Machine Learning & Deep Learning

Frameworks

Recommendation Systems

Anomaly & Fraud Detection

Time Series Forecasting

Supporting Technologies & Toolkits

Real results for real business

Dating Application

Education Business

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Recommendation Systems

Key Capabilities of Our Recommendation System Solutions

we use include:

we use include:

We build models that:

Use Cases

These include:

Use Cases

We use include:

Use Cases

Try advanced fraud prevention technologies to safeguard your assets.

Industry Use Cases of Recommendation Systems

eCommerce

Tech Stack:

Media & OTT

Tech Stack:

EdTech

Tech Stack:

Finance

Tech Stack:

Retail

Tech Stack:

Technologies We Use for Recommendation Systems, Anomaly & Fraud Detection, and Time Series Forecasting

Machine Learning & Deep Learning

Frameworks

Recommendation Systems

Anomaly & Fraud Detection

Time Series Forecasting

Supporting Technologies & Toolkits

Real results for real business

Dating Application

Education Business

Our Achievements & Certifications

Let's Connect

Let's collaborate to achieve excellence.

For Business

For Careers

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