Recommender Systems

Recommender systems (RecSys) are Artificial Intelligence-based computer agents that can help us in such tasks, filtering and suggesting those items (products, movies, books, etc.) that may be of interest to users, based on large collections of data.

ℹ Recommender Systems aim to provide useful suggestions to users.

Considerations in a Recommender System:

  • It should provide relevant and personalized information.
  • It should suggest items the user can access and find relevant.
  • It should make diverse suggestions.
  • It should introduce new items.


  • Users
  • Content
  • Ratings
  • Rating time

User information types:

  • Explicit data is generated by a user taking a direct action that indicates his/her preferences (for example, ranking the particular product)
  • Implicit behavioral data (i.e., user behavior itself) forms the majority of the user profile (for example, what kind of products user views the most)

RecSys Types:

  • Content-based Filtering System: Based on user preferences
  • Collaborative Filtering: Based on the preferences of people related to user.
    • User-based collaborative filtering
    • Item-based collaborative filtering
    • Matrix factorization methods
  • Association Rule Mining: Utilizes ARL algorithms to find patterns in data. Apriori Algorithm is often used for this type of RecSys.
  • Demographic-based: Based on specific audience segments and it’s trends
  • Utility-based: Based on usefulness of item; which is calculated from expressed preferences of the users
  • Knowledge-based: Based on recorded interactions and assumed needs and preferences; calculates match and possibilities and attempts to predict the most potent ones
    • Incorporates domain knowledge and rules to make recommendations
    • Useful in domains with specific constraints or requirements
  • Context-Aware Recommendation: Context-aware recommendation refers to a personalized recommendation approach that takes into account additional contextual information.
    • Considers additional contextual information such as time, location, and user behavior
    • Enhances the relevance and timeliness of recommendations
  • Argument-Based: Based on automatic extraction and exploitation of arguments(NLP Task) from product reviews, user blogs, social network posts, …
  • Learn to Rank (LTR): It focuses on training models to directly optimize the ranking of items for a given user. Instead of predicting user preferences or generating recommendations, LTR algorithms are designed to learn the best ranking of items based on user interactions, feedback, and historical data.
  • Hybrid Methods:
    • Combination of collaborative filtering and content-based filtering
    • Utilizes the strengths of both approaches to provide more accurate and diverse recommendations
  • Reinforcement Learning: Recommender systems that utilize Reinforcement Learning to optimize long-term user engagement and satisfaction.


  • Content Discovery
  • Dynamic Audience Insights
  • Stronger User Engagement & Retention


  • Cold start problem

Some applications of Recommender Systems:

  • E-commerce: Recommender systems are widely used in e-commerce to suggest products that customers might be interested in based on their past purchases, browsing history, and other behavior.
  • Music and Video Streaming: Recommender systems are used by music and video streaming services to suggest songs, albums, and videos that users might enjoy based on their listening or viewing history.
  • Social Media: Recommender systems are used by social media platforms to suggest people, groups, and content that users might be interested in based on their social connections and past interactions.
  • News and Content Websites: Recommender systems are used by news and content websites to suggest articles, videos, and other content that users might find interesting based on their past reading habits and interests.
  • Online Advertising: Recommender systems are used in online advertising to target ads to users based on their interests and behavior.
  • Job Matching: Recommender systems are used in job matching platforms to suggest job openings to job seekers based on their skills, experience, and preferences.
  • Travel and Hospitality: Recommender systems are used in travel and hospitality to suggest travel destinations, hotels, and activities to users based on their past travel history and preferences.
  • Healthcare: Recommender systems are used in healthcare to suggest treatments, medications, and other health-related recommendations to patients based on their medical history and current symptoms.
  • Finance: Recommender systems are used in finance to suggest investment opportunities, financial products, and other financial recommendations to users based on their financial goals and risk tolerance.

Learning Material: