Iterative Use of Weighted Voronoi Diagrams to Improve Scalability in Recommender Systems

Das, Joydeep; Majumder, Subhashis; Dutta, Debarshi; Gupta, Prosenjit

doi:10.1007/978-3-319-18038-0_47

Cited by 6 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These works do not clarify the physical meaning of these low-dimensional implicit vectors and do not consider the impact of the user's contextual factors on the recommendation results. In addition, [31], [32] leveraged the unique characteristics of the Voronoi diagram to constrain the geography of the target user and then generated the recommendation list. In addition, [36] used the image features of items to mine user preferences.…”

Section: Realate Workmentioning

confidence: 99%

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

et al. 2021

View full text Add to dashboard Cite

Section: Realate Workmentioning

confidence: 99%

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

et al. 2021

View full text Add to dashboard Cite

“…Similarly, Xue et al introduce a k-means clustering phase prior to prediction [20], and predict a rating for a user u by choosing k neighbors out of the clusters with representatives that score highly with u. Rashid et al [2] incorporate bisecting k-means clustering "to increase efficiency and scalability while maintaining good recommendation quality" in their ClustKNN algorithm. Das et al [7,6] use a DBSCAN-based algorithm to improve kNN prediction accuracy. An extensive experimental study of the effectiveness of various centroid selection methods for the k-means algorithm when used as a pre-processing step in recommendation systems is presented by Zahra et al [21], who conclude that although many approaches improve prediction accuracy and efficiency, no algorithm is "a panacea" across all data sets.…”

Section: Related Workmentioning

confidence: 99%

LiRa: A New Likelihood-Based Similarity Score for Collaborative Filtering

Strnadova-Neeley,

Buluc,

Gilbert

et al. 2016

Preprint

View full text Add to dashboard Cite

Recommender system data presents unique challenges to the data mining, machine learning, and algorithms communities. The high missing data rate, in combination with the large scale and high dimensionality typical of recommender systems data, requires new tools and methods for efficient data analysis. Here, we address the challenge of evaluating similarity between users in a recommender system, where for each user only a small set of ratings is available. We present a new similarity score, that we call LiRa, based on a statistical model of user similarity for large-scale, discrete valued data with many missing values. We show that this likelihood ratio-based score is more effective at identifying similar users than traditional similarity scores in user-based collaborative filtering, such as the Pearson correlation coefficient. We argue that our approach has significant potential to improve both accuracy and scalability in collaborative filtering.

show abstract

“…Content-based systems are developed with user ratings or data provided by clicking a link. Researchers are making recommendations with the user profiles they create using this data [7]. As the amount of information obtained by users increases, the accuracy of the results also increases.…”

Section: Introductionmentioning

confidence: 99%

A Novel Article Recommendation System Empowered by the Hybrid Combinations of Content-Based State-of-the-Art Methods

KUŞ

Keser

Okyay

2023

International Journal of Applied Mathematics Electronics and Computers

View full text Add to dashboard Cite

The initial literature reviewing step is of great importance during any scientific reporting. Nevertheless, finding relevant papers grows tough as the number of online scientific publications rapidly increases. Correspondingly, the need for article recommendation systems has emerged, which aim to recommend new papers suitable for the researchers’ interests. Using these systems provides researchers access to related publications quickly and effectively. In this study, a novel article recommendation system, which is empowered by the hybrid combinations of content-based state-of-the-art methods, is proposed. Various methods are utilized comparatively for an in-depth analysis, and user profiles are evaluated. 41,000 articles collected from the ARXIV dataset are used in the performance evaluation. In the experiments in which Word2vec and LDA are combined, Precision@50, Recall@50, and F1-score@50 achieve the highest performance with .206, .791, and .498 values, respectively. The in-depth analysis and the numerical findings justify that the proposed system is strong and promising compared to the literature.

show abstract

Iterative Use of Weighted Voronoi Diagrams to Improve Scalability in Recommender Systems

Cited by 6 publications

References 10 publications

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

LiRa: A New Likelihood-Based Similarity Score for Collaborative Filtering

A Novel Article Recommendation System Empowered by the Hybrid Combinations of Content-Based State-of-the-Art Methods

Contact Info

Product

Resources

About