AI in Recommendation Systems
AI in Recommendation Systems
AI in Recommendation Systems

Our Expertise in Developing Recommendation Systems

The nexocode team has significant experience in developing recommender systems for various applications. We have used our expertise to create solutions for clients in a range of industries, including travel and social media platforms. The systems we developed focused on recommending products, offers, articles, and more. Here are some examples of the types of recommender systems we have experience in:

Reach Out to Our Experts

If you are looking for a recommender system development company that can help you build a custom solution, nexocode is the right choice. We have years of experience in developing recommender systems, and our team of experts can help you make a solution that meets your specific business needs. Contact us to learn more and discuss your project.

Krzysztof Suwada

Krzysztof Suwada
Data Science Expert

Dorota Owczarek

Dorota Owczarek
Design Thinking Facilitator & Product Lead

Oleksandr Zakharchuk

Oleksandr Zakharchuk
Machine Learning Expert & Advisor

Recommendation Engines Across the Industries

AI in Retail and eCommerce

Retail and eCommerce

Make your customers feel like you're getting the best possible advice with every purchase. eCommerce platforms can leverage recommender systems to show personalized product recommendations, resulting in increased sales, conversions, and brand loyalty. Apart from classic relevant item recommendations, eCommerce platforms can leverage recommendations engines to bring up functionalities like trending products, styling recommendations with visual search, refill recommendations or complete the basket offers.

AI in Marketing and Advertising

Marketing and Advertising

Reach your target audience with the right message at the right time, resulting in increased sales and conversions. In the marketing and advertising industry, recommender systems are used to show personalized ads and content to users. Recommender systems can be location-based, time-based, or interest-based to target the right customer with the right message.

AI in Travel and Hospitality

Travel and Hospitality

Increase bookings for flights, hotels, restaurants, and other businesses from the travel industry. In the travel industry, recommender systems are used to suggest hotel accommodations, restaurants, and tourist attractions based on customer preferences. This helps customers find the perfect vacation spot.

AI in Banking, Financial Services and Insurance

Banking, Financial Services and Insurance

Offering the right products and services to the right customer at the right time will result in increased sales and conversions. In the banking, financial services, and insurance industry, recommender systems are used to show personalized products and services to customers. Recommendation engines can be used to suggest investment opportunities, financial products, insurance policies, and credit card offers.

AI in Entertainment and Media

Entertainment and Media

Increase consumption of recommended content and enhance the overall user experience by letting your customers find content that they will enjoy. In the entertainment industry, recommender systems are used to suggest movies, music, social media content, and books.

AI in Energy and Telecommunications

Energy and Telecommunications

Increase sales and conversions by suggesting the right products and services that match customer expectations. In the energy and telecommunications industry, recommender systems are used to show personalized products and services to customers. Recommendation engines can be used to suggest energy-saving tips, green energy options, and telecom or energy plans.

Custom Recommendation Systems Can Support Your Business in Many Ways

Recommendation engines can be used to support your business in many ways. Some of the benefits of having a recommender system include:

  • Improved customer satisfaction and engagement by providing personalized content, products, or services
  • Increased revenue by providing accurate recommendations that result in more sales and conversions
  • Reduced costs by automating the process of making recommendations based on relevant information
  • Improved customer retention by recommending items that keep customers coming back for more
  • Targeted marketing campaigns by understanding what your customers want or need and what their behaviors are
  • Engaging user experience paths that are personalized to meet user preferences and needs

Our Case Studies

To give you a better idea of the type of work we do, here are some case studies of recommender system development projects we have completed for past clients:

Social Media Platform

Topic extraction and personalization models for idea exchange system

Developing an AI-based system that is able to extract and classify interesting ideas from articles and books and recommend them to social network users?
Challenge Accepted!

Natural Language Processing Recommender System

Travel Sector

AI-Powered Corporate Booking Platform

Developing a platform that enables smooth corporate travel booking and management?
Challenge Accepted!

Custom Software Development Recommender System Natural Language Processing

See more projects
What Nexocode Can Offer as a Boutique AI Development Company?

How Can We Help - Our Recommendation System Development Services

If you're looking to build your own recommendation engine, our team can help. At nexocode, we offer a wide range of recommendation system development services that can be tailored to your specific needs and requirements. nexocode's offering covers end-to-end recommendation engine implementation services from strategy, advisory, and consulting to development, integration, and ongoing production support.

AI Design Sprint

Strategic Consulting and Advisory

Unleash the artificial intelligence potential for your business. We can help you assess the feasibility of using computer vision algorithms to achieve your desired business outcomes and develop a roadmap for implementing your recommendation system project. We organize AI Design Sprint workshops focused on recommendation engines where you can check the potential of developing personalized experiences and create new value for your business through digital transformation.

Proof of Concept Development

Data Analysis and Preprocessing

Spend less time worrying about data preparations and more time building your business. Recommendation engines need high-quality data to be effective. We can help with data labeling to prepare your unstructured data so that you can get the most out of your recommendation system. We can augment your data sets by building generative adversarial network solutions. Our Data Scientists will support you in conducting exploratory data analysis and advise on building a data strategy.

Data Quality Audit

Recommender System Development

Delight your customers with perfectly tailored recommendations that make them feel special. We offer a full range of recommendation engine implementation services, from developing custom algorithms to deploying and integrating your solution. We have experience with a wide range of recommender systems, including content-based, collaborative filtering, and hybrid recommender systems. We will work with you to select the right algorithms for your data and business goals.

Building Data Systems

Custom Product Development

Transform the way your customers interact with your product. In addition to algorithm development, our AI Product Managers will focus on delivering real user value through an intuitive and user-friendly interface for your recommender system. We understand that your users will only engage with the system if it is easy to use, so we put a lot of thought into designing user flows that are both logical and visually appealing. We follow an agile development process so that you can get a high-quality product that is delivered on time and within budget.

Custom AI Software Development

Recommendation Engine Integration and Maintenance Services

Keep your recommender system running smoothly with our expert support. We will help you integrate your recommender system into your existing IT infrastructure and make sure it runs smoothly. We offer a full range of integration and maintenance services, from data migration to performance tuning and optimization. We also offer 24/7 monitoring and support to make sure your recommender system is always up and running.

A development process that brings AI results in weeks, not years


2 hours

Explain your business and share your pain points to gain insights into AI capabilities and an approach designed by our nexocode experts.

AI Design

2 days

Identify a high-impact business problem and collaborate with nexocode experts to propose an AI solution.

Proof of AI

4 – 12 weeks

Rapidly build an AI model that solves your problem and subsequently test machine learning algorithms on your datasets.

AI Application Deployment
in Production

3 – 9 months

Build automated pipelines, scale, and deploy your artificial intelligence app into production. Adjust algorithms to meet your desired benchmarks.

Recommendation System Solutions Development - Your Questions Answered

What is a recommendation system?

A recommendation system, or a recommender system, is a subclass of information filtering systems that seeks to predict the "rating" or "preference" a user would give to an item. Recommender systems are utilized in a variety of areas, with commonly recognized examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single input, like music, or multiple inputs within and across platforms like news, past purchases, on-site user behavior, and search queries.

How do recommender systems work?

Recommender systems typically produce recommendations using one or more of the three approaches: content-based, collaborative filtering, or hybrid systems.

Content-based filtering recommender systems analyze items (music, movies, articles, products, touristic attractions, etc.) to understand the characteristics of those items and recommend similar or recommended items to users. This approach is generally used when there is sufficient data available about the items in question.

Collaborative filtering recommender systems, on the other hand, produce recommendations based on the collective behavior of a group of users and user similarity. This approach is generally used when there is insufficient explicit data about the items in question and relies on the "wisdom of the crowd" to make recommendations based on similar users.

Hybrid recommender systems use a combination of content-based and collaborative filtering approaches to make recommendations. These systems help improve recommendations from inadequate, insufficient, and infrequent datasets or are applied in solutions where both approaches matter to provide the highest level of personalization.

What are the benefits of applying recommendation systems?

There are many benefits to using recommendation engines in the business, including:

  • improving customer engagement and retention,
  • increasing sales and conversion rates,
  • reducing costs associated with customer acquisition through targeted marketing,
  • improving customer satisfaction with personalized experiences,
  • personalized suggestions for related, relevant, and alternative products.
What is the difference between machine learning and recommendation algorithms?

Machine learning is a subfield of artificial intelligence that deals with the design and development of algorithms that can learn from data. Recommendation algorithms are a type of machine learning algorithms that are used to make recommendations.

Will a recommendation system bring value to my company?

Thoroughly understanding your business needs is critical to determining whether a recommendation system project will bring value to your company. As a general statement, recommendation engines can be applied to many industries and business functions to improve sales, conversion, and customer engagement.

There is no guarantee, however, that a custom recommendation software will get this expected value or the total cost will not be too high to handle. That is why a detailed planning phase and an iterative approach are crucial in every AI-based project. Only in this way will you be able to predict the cost, income, and break-even points of your development. There are ways to reduce the risk of not getting value from solutions that use AI. nexocode's approach is based on a deep understanding of the needs of the projects and the possibilities of current AI technologies coupled with the iterative approach. Our project methodology focuses on delivering value as early as possible and starting small with AI Proof of Concept development.

You can find out more about how to turn AI into a profitable investment in our article on the ROI of AI. Our team will help you leverage the latest AI tools and methodologies to create practical data-driven applications that deliver real business value.

How does nexocode approach recommendation engine development?

At nexocode, we take a data-driven approach to developing recommendation engines. We start by understanding your needs and goals and then collecting and preparing the necessary data. Once the data is ready, we use a variety of machine learning and deep learning techniques to train the recommendation engine. We then evaluate the performance of the engine and iteratively improve it until it meets your expectations. Finally, we deploy the engine and integrate it with your existing systems.

How much data do I need for a recommendation system?

There is no one-size-fits-all answer to this question. The amount of data required for the recommendation engine will depend on the type of task you are trying to accomplish, the complexity of the task, and the available resources. In general, however, it is advisable to have as much data as possible when working on deep learning projects, as more data will allow the algorithm to learn and improve its performance.

Our Data Scientists are experienced in providing preliminary Data Quality Audits and in preparing a dedicated data collection strategy for our clients.

I have a lot of data. Can nexocode help me clean and prepare it for the recommendation system?

We are experienced in working with a wide variety of data sources and formats. We can help you clean and prepare your data for recommendation engines. Our team can also help you not only with preprocessing or data labeling but also with designing and implementing a custom data collection strategy tailored to your specific needs.

How much does it cost to develop a computer vision solution?

Wisely navigating the complexities of recommendation system development requires specialized expertise and experience. At nexocode, we have a team of experienced data scientists and machine learning engineers who can help you evaluate your needs and determine an optimal approach for your project. The cost of developing recommendation system will depend on a number of factors, including the complexity and scope of the project, the technology used or the need for additional integrations. Whichever approach you choose, we will work with you to find the right balance between cost and quality and ensure that your project stays on track. To estimate your custom recommendation product idea, get in touch today!

Which programming languages and frameworks are used to build recommendation systems?

Choosing which technology should be used to build recommendation engines will depend on the specific task you are trying to accomplish and the technology that you want to use or already use within other services. At nexocode, we have experience using a variety of different programming languages and frameworks, including Python, Keras, TensorFlow, Google Cloud ML Engine, LensKit, and Spotlight. We prefer a technology-agnostic approach to make sure to select the recommendation system techniques, platforms, and libraries that work best for your task.

How to integrate a recommendation engine with internal systems?

Many companies already have a large investment in internal systems and data. nexocode can help you leverage this investment by integrating a recommendation engine with your existing systems. This will allow you to use your data more effectively and get the most out of your investment.

Do you want to talk
about your idea?

Dorota Owczarek - AI Product Lead
Dorota Owczarek AI Product Lead schedule a call

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