A recommender system (RS) is software that provides suggestions to a user in decision making process. The decision making may be for commercial purposes, personalized applications or simple information retrieval. These systems have become an important component of almost all applications that relate to some form of information retrieval and processing by using various search techniques. This chapter provides the background to theoretical foundations of RSs. We start with a traditional approach and discuss some commonly used definitions of RSs. After justifying the need for such systems in the current scenario of information overload, application areas where RSs can be useful and productive are discussed. It includes the discussion on both existing and possible future areas of applications. The list presented is not exhaustive, as many areas have opted to add value to their applications by integrating with some form of RS. The next section gives a brief overview of the phases through which RS passes in order to perform its function. The types of RSs are discussed giving their advantages and disadvantages, and content-based recommenders, collaborative filtering (CF) based recommenders, hybrid recommenders, image-based recommenders, and graph database (GDB)-based recommenders are discussed in detail. After a brief overview of the problems identified with the current RSs, some datasets are listed that may be used for research and evaluation of various RSs.

Chapter Contents:

• 2.1 Introduction
• 2.1.1 Definitions of RSs
• 2.1.2 The need for an RS
• 2.2 Applications of RSs
• 2.2.1 Current use of RSs
• 2.2.1.1 Product recommendation (e-commerce)
• 2.2.1.2 Movie or music recommendation (entertainment)
• 2.2.1.3 Scholarly search and news articles
• 2.2.1.4 Services
• 2.2.2 More areas for RSs
• 2.2.2.1 Recommend courses for students
• 2.2.2.2 Perform career counselling
• 2.3 Algorithms and theoretical foundations of RSs
• 2.3.1 Phases of RSs
• 2.3.1.1 Information collection
• 2.3.1.2 Learning phase
• 2.3.1.3 Prediction/recommendation phase
• 2.3.2 Types of RSs
• 2.3.2.1 Content-based recommenders
• 2.3.2.2 CF recommenders
• 2.3.2.3 Hybrid recommenders
• 2.3.2.4 Image-based recommenders
• 2.3.2.5 RSs using GDBs
• 2.3.3 Datasets for recommendations
• 2.3.3.1 MovieLens
• 2.3.3.2 Jester
• 2.3.3.3 BookCrossing
• 2.3.3.4 Amazon product data
• 2.3.3.5 YahooWebscope datasets
• 2.4 Problems related to RSs
• 2.4.1 Data sparsity problem
• 2.4.2 Cold start problem
• 2.4.3 Scalability
• 2.4.4 Overspecialization or diversity problem
• 2.4.5 Vulnerable to attacks
• References

Inspec keywords: recommender systems; collaborative filtering; content-based retrieval; image retrieval

Other keywords: hybrid recommenders; image-based recommenders; content-based recommenders; information retrieval; big data; collaborative filtering based recommenders; recommender systems; search techniques; decision-making; graph database based recommenders

Subjects: Search engines; Information analysis and indexing; Information retrieval techniques; Information networks