Concept - Basics
In the following sections, we will more systematically introduce the following concepts:
📄️ Challenges
The construction of effective Recommender Systems (RS) is a complex process, mainly due to the nature of RSs which involves large scale software-systems and human interactions. Iterative development processes require deep understanding of a current baseline as well as the ability to estimate the impact of changes in multiple variables of interest. Simulations are well suited to address both challenges and potentially leading to a high velocity construction process, a fundamental requirement in commercial contexts. Recently, there has been significant interest in RS Simulation Platforms, which allow RS developers to easily craft simulated environments where their systems can be analyzed.
📄️ Collaborative Filtering
Similarity methods
📄️ Evaluation
Online vs Offline Evaluation
📄️ User Feedback
Explicit vs. implicit users feedback
📄️ Processes
Retrieval and Ranking
📄️ Session-based Recommenders
Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations, therefore, have to be made solely based on the observed behavior of a user during an ongoing session.
📄️ Tasks
Top-K Recommendation
📄️ Types of Recommender Systems
Group Recommender System