Journal ArticleDOI
A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining
TLDR
The proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.About:
This article is published in Future Generation Computer Systems.The article was published on 2017-07-01. It has received 195 citations till now. The article focuses on the topics: Recommender system & Domain knowledge.read more
Citations
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Journal ArticleDOI
A service recommendation system based on rough multidimensional matrix in cloud-based environment
TL;DR: Wang et al. as mentioned in this paper proposed a service recommendation system in cloud-based environment that helps a user to select the best services from different cloud providers that match user requirements, the new system combines items' attributes information (decision tags) and user's attribute information(decision times), achieves high performance and alleviates the cold-start problems of recommendation systems.
Journal ArticleDOI
TMAP: Trip Recommendation System Accommodating the Mobile Tourist Preferences
Ferdi Sönmez,Arif Mehmet Akay +1 more
TL;DR: Tmap is a location based application which enables tourists or any local users with a facility to explore and get information about nearby must see places according to his/her personal point of interest.
Journal ArticleDOI
Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000-2021
TL;DR: In this paper , a bibliometric analysis that focuses on social media based on existing publications was conducted by identifying SMRS-related publications and scientometric indicators to assess the growth rate, including the relative growth rate (RGR), doubling time (DT), and the field-normalized citation score (NCSf), for citation analysis.
Journal ArticleDOI
An improved adaptive learning path recommendation model driven by real-time learning analytics
Nisha S. Raj,V.G. Renumol +1 more
TL;DR: In this paper , a learning path recommendation approach focused on knowledge building and learning performance analysis is proposed, where the difficulty level of the learning resources is tuned based on the real-time performance analysis of the students.
Journal ArticleDOI
A probabilistic Profile Reliability Approach to Improve the Richness of User's Interests Based on Social Information
TL;DR: A Probabilistic approach to handle conflicts by detecting reliable profiles so as to improve the richness of user’s interests and takes into account the organizational aspects of interests in terms of their evolutionary aspect (freshness and popularity) as well as their semantic relationships.
References
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Book
Introduction to Information Retrieval
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.