• Skip to primary navigation
  • Skip to main content
Fellow Consulting AG

Fellow Consulting AG

where people work together

  • Products & Services
    • Digital Transformation
    • Infor OS
    • Ephesoft Transact – IDM Capture
      • FellowKV²-Plugin
      • TableExtraction² Plugin
    • Marketing & CRM
      • SugarCRM
        • Sales & Service
          • Sugar Sell
          • Sugar Serve
          • Business Intelligence – Sugar Discover
        • Marketing
          • Sugar Market
        • Customer Data Management
        • Appointment Calendar
        • Follow-ups
        • Free SugarCRM 30 days trial
      • Oktopost – Social Marketing
      • Sugar Market
  • Customer Success Stories
    • MVTec – SugarCRM for a global manufacturer
    • Bet3000 – SugarCRM for Gambling
    • Coloplast – Mobile CRM for Life Science
    • Diversey – Mobile CRM and Maintenance for Cleaning and Hygiene
    • Diversey – Online Shop implementation
    • Jochen Schweizer – SugarCRM for Leisure
    • Acnos Pharma GmbH streamlines workflow
  • Fellow
    • About Us
    • Career: We are hiring!
    • Contact Us
    • Change Optin
    • Partners
  • Show Search
Hide Search

Recommender Systems

Inês Brandão · 11. November 2019 ·

Teilen

Recommender system is a data filtering tool used to predict users interests or preferences, according to their historical data stored on a database. It is a way to understand your clients’ needs based on their past activities and make suggestions about what they will probably like, improving customer experience and satisfaction.

Also called as a recommendation engine, this feature developed with AI (artificial intelligence) allows companies to be much more assertive offering its products or services to customers according to their historical behavior (clicks, purchases, likes, rates, etc.). Recommender systems have a very accurate process, once it utilizes machine learn algorithms.

This computer program is applied in many different channels, such as commercial apps, e-commerce, social media, content-based services and others. It is used to facilitate users’ choices, once they will get suggestion based on their preferences and tastes previously shown.

There are some different approaches which can drive the recommendation engines:

Collaborative filtering – This is based on previous information about customers activities. The engine analyzes data and makes the assumption that if users had some types of agreements in the past, they will keep having the same in the future. 

It tracks their behaviors and preferences to identify patterns and provide recommendations based on similarity of users. One good example is the recommendation made by Netflix or Spotify – which offers users types of films or music according to their previous preferences and other users preferences similarly – they consider that if User1 likes items A, B and C, while User2 likes items A and B, there are big chances that User2 will also like item C.

Content-based filtering – In this case, the system uses its knowledge of each product to recommend new products. It knows the attributes that all the products have and match them with customers’ previous preferences. As an example, we can think about Amazon – You buy a drama genre book from author X, then the system will recommend you another drama genre book, which was written by the same author because the engine understands that they have a lot of attributes in common.

Hybrid recommendation systems – This approach uses information from both collaborative and content-based filtering, which are user-item interactions and items’ characteristics. Its performance can be even better than the other two single approaches, because it will cover any gap that exists in each of them separated. This system is much more complete and ensure a better result for users.

Understanding recommender system, it is possible to see how good and powerful this feature can be for businesses and also for users. It provides much time saving and precision for customers when they are looking for some product or entertainment and helps companies to be much more effective on their offers.


Teilen

News AI, Artificial intelligence, Recommender system

Additional blogs:

Digitization forces low-code development

Did you know that over 90% of the total number of invoices issued globally are still processed manually?

Yes, you read that right. Less than 10% of all invoices are automatically processed! This takes a huge toll on AP departments.

Digitalization

Digitalization: Shifting from short-term continuity to long-term transformation

The pandemic has served as a wake-up call for many finance organizations and – if they hadn’t already started – they implemented more digital processes. 
 
However, it is important to ensure these new workflows are sustainable. Here’s how:

Doc² Integration to Infor

DOC² is the newest document capturing software and solution developed by Fellow Consulting AG. It’s a completely AI and cloud-based solution that enables optical character recognition (OCR) capabilities and provides with a more automatic and self-learning capturing process. Most importantly is that apart from the extraction of meaningful data, it can also offer numerous connection functions for ERP systems such as Infor, SAP, Microsoft and […]

What is Deep Learning

What is Deep Learning?

The Magic of deep learning. Deep learning is a sub-area of machine learning and uses neural networks and large amounts of data. The learning methods are based on the functioning of the human brain and result in the ability to make one’s own prognoses or decisions. On the basis of existing information and the neural […]

90% manual work

Did you know that over 90% of the total number of invoices issued globally are still processed manually?

Yes, you read that right. Less than 10% of all invoices are automatically processed! This takes a huge toll on AP departments.

  • Contact Us
  • Privacy policy
  • Imprint
  • AGB