Nearest biclusters collaborative filtering framework with fusion

Kant, Surya; Mahara, Tripti

doi:10.1016/j.jocs.2017.03.018

Cited by 27 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It not only models the interaction between two items by using similarity metrics but also models the interaction among all interacted item pairs by using neural networks. Reference [23] presented that there is a method to combine item-based CF and user-based CF according to a new similarity measure.…”

Section: Neighborhood-based Recommendation Systemmentioning

confidence: 99%

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

et al. 2021

View full text Add to dashboard Cite

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.

show abstract

Section: Neighborhood-based Recommendation Systemmentioning

confidence: 99%

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…52 Moreover, further research addressed the data sparsity problem with biclusters to solve very low ratios between actual data and predicted data by using a hybrid user and item method. 56 Other studies have implied that the k-means algorithm does not improve the efficiency problem of recommendation systems and instead demonstrated the use of bioinspired clustering techniques and their implementation in real-life trip recommendation engines. 57 Furthermore, the categorical data problem has been addressed and explained as a way to apply the classical k-means algorithm.…”

Section: Clusteringmentioning

confidence: 99%

AutoML classifier clustering procedure

Koren

Hallin

Koren

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

Recommendation systems are one of the main applications of machine learning (ML) used across different industries. This paper presents a new automated machine learning (AutoML) method of providing recommendations by processing data sets using ML algorithms, targeting, and offering cluster recommendations for new observations and as a new decision support method. The AutoML conducts a complete procedure and includes analysis and division of data into an efficient number of clusters. We apply the k‐means, using the elbow method to calculate costs per cluster, followed by analyzing the allocation of the data into the clusters, thus providing a method for prediction and for allocating new observations to the relevant clusters (knn). This study includes two experiments using the complete AutoML procedure conducted on a data set, with more than two million records and dozens of attributes. This was done to demonstrate how the AutoML method can be implemented and successfully run with a high‐capacity analysis procedure. The motivation was to analyze, examine, assign, and integrate new observations into existing clusters that have been defined. The results showed that the AutoML method provided efficient recommendations for new observations with an accuracy rate of 99.99%. Hence, the AutoML procedure can offer a full system for any organization to efficiently split existing data into clusters, assign to clusters, and predict the cluster allocation of new observations. The significant contribution of this study is a simple method that can achieve fast and high accuracy clustering for ongoing (new) classified data acquired by an organization.

show abstract

“…Various approaches have been put forward to simultaneously address the abovementioned challenges. Initial works focused primarily on content-based filtering (CB) (Popescul et al 2001, Melville et al 2002 and collaborative filtering (CF) (Popescul et al 2001, Melville et al 2002, Yu et al 2017b, Kant and Mahara 2018, among which models like time weight CF (Ding and Li 2005), timeSVD++ (Koren 2009), TMF and BTMF (Zhang et al 2014), TGSC-PMF (Ren et al 2017), CTF-ARA (Si et al 2017) can be pointed out. For example, STELLAR was proposed as a tensor-based algorithm for successive POI recommendations in LBSNs (Zhao et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

Taheri

Farnaghi

Alimohammadi

et al. 2021

Journal of Spatial Science

View full text Add to dashboard Cite

Previous studies neglected the impact of shared spatio-temporal interests in the Point of Interest (POI) recommendation. The proposed GHTG-ERWR method simultaneously models shared spatio-temporal preferences of users, the dynamics of users' behavior, and the geographical effect for POI recommendation. It is composed of an Extended Random Walk with Restart (ERWR) algorithm that works on a Geographical-temporal Hybrid Tripartite Graph (GHTG). The method overcomes shortcomings of prior approaches by employing users' joint preferences over time and space through the auxiliary information derived from location-session and sessionlocation edges. Experimental results on Gowalla and Weeplaces datasets proved the feasibility of the approach.

show abstract

Nearest biclusters collaborative filtering framework with fusion

Cited by 27 publications

References 24 publications

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

AutoML classifier clustering procedure

Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

Contact Info

Product

Resources

About