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Deploying Different Clustering Techniques on a Collaborative-based Movie Recommender

https://doi.org/10.1109/syscon48628.2021.9447139

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-41671-2_98openalex-snapshot

A Survey on Density-Based Clustering Algorithms

inferredcites →https://doi.org/10.1007/978-981-15-1084-7_30openalex-snapshot

A Faster Fuzzy Clustering Approach for Recommender Systems

inferredcites →https://doi.org/10.1007/s00521-015-2142-2openalex-snapshot

A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method

inferredcites →https://doi.org/10.1007/s10586-017-0807-6openalex-snapshot

An improved clustering-based collaborative filtering recommendation algorithm

inferredcites →https://doi.org/10.1007/s11042-016-4078-7openalex-snapshot

An effective web page recommender system with fuzzy c-mean clustering

inferredcites →https://doi.org/10.1007/s11704-019-9059-3openalex-snapshot

A survey of density based clustering algorithms

inferredcites →https://doi.org/10.1016/j.cie.2017.05.016openalex-snapshot

A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques

inferredcites →https://doi.org/10.1016/j.eswa.2020.113346openalex-snapshot

Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks

inferredcites →https://doi.org/10.1016/j.future.2015.01.001openalex-snapshot

A survey of anomaly detection techniques in financial domain

inferredcites →https://doi.org/10.1016/j.knosys.2015.02.016openalex-snapshot

Simultaneous co-clustering and learning to address the cold start problem in recommender systems

inferredcites →https://doi.org/10.1016/j.measurement.2016.05.058openalex-snapshot

User based Collaborative Filtering using fuzzy C-means

inferredcites →https://doi.org/10.1016/j.neucom.2017.06.053openalex-snapshot

A review of clustering techniques and developments

inferredcites →https://doi.org/10.1016/j.patcog.2017.05.017openalex-snapshot

Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters

inferredcites →https://doi.org/10.1016/j.procs.2016.04.265openalex-snapshot

DENCLUE-IM: A New Approach for Big Data Clustering

inferredcites →https://doi.org/10.1109/access.2020.3026310openalex-snapshot

Enhancing the Role of Large-Scale Recommendation Systems in the IoT Context

inferredcites →https://doi.org/10.1109/ciact.2017.7977363openalex-snapshot

Collaborative filtering-based recommender system: Approaches and research challenges

inferredcites →https://doi.org/10.1109/ciss48834.2020.1570617370openalex-snapshot

Improving Recommendation Performance with Clustering and Missing Value Prediction

inferredcites →https://doi.org/10.1109/confluence.2019.8776969openalex-snapshot

Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor

inferredcites →https://doi.org/10.1109/ic3i.2014.7019655openalex-snapshot

Clustering-based recommender system using principles of voting theory

inferredcites →https://doi.org/10.1109/ic4.2015.7375707openalex-snapshot

Recommender system using fuzzy c-means clustering and genetic algorithm based weighted similarity measure

inferredcites →https://doi.org/10.1109/icbda.2018.8367721openalex-snapshot

Clustering of public transport operation using K-means

inferredcites →https://doi.org/10.1109/icicct.2017.7975168openalex-snapshot

Implementation and comparison of K-means and fuzzy C-means algorithms for agricultural data

inferredcites →https://doi.org/10.1109/icspcom.2016.7980578openalex-snapshot

Resolving Cold Start problem in recommendation system using demographic approach

inferredcites →https://doi.org/10.1109/iemtronics51293.2020.9216391openalex-snapshot

IoT-based Recommendation Systems – An Overview

inferredcites →https://doi.org/10.1109/spaces.2018.8316341openalex-snapshot

Unsupervised learning algorithms for MRI brain tumor segmentation

inferredcites →https://doi.org/10.1109/tetc.2014.2330519openalex-snapshot

A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

inferredcites →https://doi.org/10.1109/tit.2014.2346205openalex-snapshot

Improved Graph Clustering

inferredcites →https://doi.org/10.1145/2645710.2645772openalex-snapshot

Social collaborative filtering for cold-start recommendations

inferredcites →https://doi.org/10.1145/3299815.3314454openalex-snapshot

Boosting Recommendation Systems through an Offline Machine Learning Evaluation Approach

inferredcites →https://doi.org/10.1155/2016/1879327openalex-snapshot

Radar Emission Sources Identification Based on Hierarchical Agglomerative Clustering for Large Data Sets

inferredcites →https://doi.org/10.1371/journal.pone.0144059openalex-snapshot

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

inferredcites →https://doi.org/10.17485/ijst/2016/v9i3/75971openalex-snapshot

A Survey on Clustering Techniques for Big Data Mining

inferredcites →https://doi.org/10.19101/ijacr.2017.729004openalex-snapshot

Movies recommendation system using collaborative filtering and k-means

inferredcites →https://doi.org/10.3390/app10217748openalex-snapshot

Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities

inferredcites →https://doi.org/10.5120/ijca2015904111openalex-snapshot

A Movie Recommender System: MOVREC

inferredcites →https://doi.org/10.5120/ijca2016907841openalex-snapshot

Clustering Techniques and the Similarity Measures used in Clustering: A Survey

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4172645openalex-extracted

An Emotion-Based Design Approach to Tackle Cold Start Challenge of Movie Recommendation System

inferred← citeshttps://doi.org/10.1007/978-3-031-67758-8_3openalex-snapshot

Analysis of Customer Behavior

inferred← citeshttps://doi.org/10.1007/978-3-032-17187-0_6openalex-snapshot

Demographic-KM: An Optimized Movie Recommendation System Using Demographic Information and K-Means Clustering

inferred← citeshttps://doi.org/10.1007/978-981-97-7862-1_29openalex-snapshot

Comprehensive Review of Collaborative Filtering Techniques

inferred← citeshttps://doi.org/10.1109/access.2021.3122098openalex-snapshot

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

inferred← citeshttps://doi.org/10.1109/aiiot58121.2023.10174373openalex-snapshot

Forecasting The Price of the Flight Tickets using A Novel Hybrid Learning model

inferred← citeshttps://doi.org/10.1109/icces63552.2024.10859332openalex-snapshot

Movie Recommendation System using DBSCAN Algorithm

inferred← citeshttps://doi.org/10.1109/itc-egypt55520.2022.9855742openalex-snapshot

Multi-Context Recommendation Systems (CARS) in Autonomous Driving and Other Applications

inferred← citeshttps://doi.org/10.1109/syscon53536.2022.9773925openalex-snapshot

Context-Aware Recommendation Systems Using Consensus-Clustering

inferred← citeshttps://doi.org/10.1109/tcss.2022.3223516openalex-snapshot

MCARS-CC: A Salable Multicontext-Aware Recommender System

inferred← citeshttps://doi.org/10.1145/3605423.3605432openalex-snapshot

Bengali Movie Recommendation System using K Nearest Neighbor and Cosine Similarity

inferred← citeshttps://doi.org/10.1145/3649901openalex-snapshot

Artwork Recommendations based on User Preferences: Integrating Clustering Analysis with Visual Features

inferred← citeshttps://doi.org/10.30880/jscdm.2023.04.01.001openalex-snapshot

Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

inferred← citeshttps://doi.org/10.32920/25413835openalex-snapshot

MCARS-CC: A Scalable Multi-context-aware Recommender System

inferred← citeshttps://doi.org/10.32920/25413835.v1openalex-snapshot

MCARS-CC: A Scalable Multi-context-aware Recommender System

inferred← citeshttps://doi.org/10.5753/jisa.2024.3851openalex-snapshot

Customer segmentation in e-commerce: a context-aware quality model for comparing clustering algorithms

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