An algorithm for density enrichment of sparse collaborative filtering datasets using robust predictions as derived ratings
D Margaris, D Spiliotopoulos, G Karagiorgos… - Algorithms, 2020 - mdpi.com
Collaborative filtering algorithms formulate personalized recommendations for a user, first by
analysing already entered ratings to identify other users with similar tastes to the user …
analysing already entered ratings to identify other users with similar tastes to the user …
Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering
D Margaris, K Sgardelis, D Spiliotopoulos… - Big Data and Cognitive …, 2024 - mdpi.com
Collaborative filtering is a popular recommender system (RecSys) method that produces
rating prediction values for products by combining the ratings that close users have already …
rating prediction values for products by combining the ratings that close users have already …
Improving collaborative filtering's rating prediction accuracy by introducing the experiencing period criterion
Collaborative filtering algorithms take into account users' tastes and interests, expressed as
ratings, in order to formulate personalized recommendations. These algorithms initially …
ratings, in order to formulate personalized recommendations. These algorithms initially …
[PDF][PDF] Collaborative Filtering Recommender System pada Virtual 3D Kelas Cendekia
AS Wardana, MIA Timur - IJEIS (Indonesian Journal of Electronics and …, 2018 - core.ac.uk
Kelas Cendekia merupakan suatu konsep proses pembelajaran modern dimana pengguna
dapat melakukan proses pembelajaran secara kolaboratif dimanapun dan kapanpun …
dapat melakukan proses pembelajaran secara kolaboratif dimanapun dan kapanpun …
Improving collaborative filtering's rating prediction coverage in sparse datasets through the introduction of virtual near neighbors
D Margaris, D Vasilopoulos… - 2019 10th …, 2019 - ieeexplore.ieee.org
Collaborative filtering creates personalized recommendations by considering ratings
entered by users. Collaborative filtering algorithms initially detect users whose likings are …
entered by users. Collaborative filtering algorithms initially detect users whose likings are …
Improving collaborative filtering's rating prediction accuracy by introducing the common item rating past criterion
D Margaris, D Vasilopoulos… - 2019 10th …, 2019 - ieeexplore.ieee.org
Collaborative filtering formulates personalized recommendations by considering ratings
submitted by users. Collaborative filtering algorithms initially find people having similar …
submitted by users. Collaborative filtering algorithms initially find people having similar …
A mixed-reality interaction-driven game-based learning framework
In the modern information society, learning is no longer just about obtaining factual
knowledge, but more about general skills on how and where to apply available knowledge …
knowledge, but more about general skills on how and where to apply available knowledge …
Policy making analysis and practitioner user experience
This article presents the work on social media analysis-driven policy-making platforms that
are powered by classic social media analysis technologies, such as policy modelling …
are powered by classic social media analysis technologies, such as policy modelling …
Semantics-driven conversational interfaces for museum chatbots
This work addresses the challenges of creating usable and personalized conversational
interfaces for broad, yet applicable, domains that require user engagement and learning …
interfaces for broad, yet applicable, domains that require user engagement and learning …
A user interface for personalising WS-BPEL scenarios
Due to the huge volume of web services available, both locally and in the cloud, the
performance of users and systems need significant research attention. Since WS-BPEL is …
performance of users and systems need significant research attention. Since WS-BPEL is …