Recommender Systems: Algorithms, Evaluation and Limitations (original) (raw)

A Survey of Recommender Systems Techniques, Challenges and Evaluation Metrics

Recommender systems are software applications that belong to a class of personalized information filtering technologies that aim to support decision making in large information space. There are various techniques being used to achieve this goal in traditional and mobile recommender systems. The recommender systems techniques are usually classified in four main categories: Collaborative Filtering (CF), Content Based Filtering (CBF), Knowledge Based Filtering (KBF) and Hybrid Filtering (HF). In this paper an overview of these techniques, challenges and evaluation metrics of recommender systems is discussed.

Recommender System: Collaborative Filtering of e-Learning Resources

2018

The significant amount of information available on the web has led to difficulties for the learner to find useful information and relevant resources to carry out their training. The recommender systems have achieved significant success in the area of e-commerce, they still have difficulties in formulating relevant recommendations on e-learning resources because of the different characteristics of learners. Most of the existing recommendation techniques do not take these characteristics into account. This problem can be mitigated by including learner information in the referral process. Currently many recommendation techniques have cold start problems and classification problems. In this paper, we propose an ontology-based collaborative filtering recommendation system for recommending learners' online learning resources based on a decision algorithm (DA). In our approach, ontology is used to model and represent domain knowledge about the learner and learning resources. Our approa...

A Survey of Recommender Systems: Approaches and Limitations

2013

Recommendation as a social process plays an important role in many applications as WWW has created the universe as a global village, with an explosive growth of enormous information. The paper presents an overview of the field of recommender systems along with the description of various approaches that are being used for generating recommendations. Recommendation techniques can be classified in to three major categories: Collaborative Filtering, Content Based and Hybrid Recommendations. The paper elaborates these approaches and discusses their limitations by describing the major problems suffered by recommendation methods. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for recommendation techniques, which can be served as a roadmap for research and practice in this area.

Hybrid content and collaborative filtering based recommendation system for e-learning platforms

Bulletin of Electrical Engineering and Informatics , 2022

Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of elearning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to generalize across content sources and content types. In this work, we present a hybrid recommendation system that combines the desirable characteristics of collaborative filtering, as well as from content-based filtering, for the task of recommending course content/curriculum to users of an e-learning system. Our recommendation easily incorporates changing user profiles (as learners step through course content) and also generalize across content sources (courses taught by various departments) and types. We apply our system on a real dataset comprising 111 students organized into interdisciplinary groups. Our results showcase the clear benefits that our hybrid recommendation system enjoys, showing more than 30 percentage points of improvement over conventional filtering techniques.

Recommender Systems: An Overview

AI Magazine

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and id...