Improving scalability issues in collaborative filtering based on collaborative tagging using genre interestingness measure

Banda, Latha; Bharadwaj, Kamal Kant

doi:10.1049/cp.2012.2537

Cited by 3 publications

(1 citation statement)

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“…Lots of research works in the field of page recommendation system [30][31][32][33][34][35] have been carried out earlier. In this section, some of the earlier works related to collaborative labeling of web pages for easy searching and recommendation to new users are discussed briefly.…”

Section: Review Of Related Workmentioning

confidence: 99%

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

Banda¹,

Bharadwaj²

2014

IJCIS

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Collaborative labeling portrays the process by which numerous users put in metadata in the form of keywords to shared data. Nowadays, collaborative labeling has grown in reputation on the web, on sites that permit users to label bookmarks, photographs and other details. It has been recently become useful and well known as one effective way of classifying items for future search, sharing information, and filtering. So, as to predict the future search of users, we propose a novel collaborative tagging-based page recommendation algorithm using fuzzy classifier. The method consists of three phases: Grouping, Rule Generation Phase and Page Recommendation Phase. In the proposed method, we calculate the resemblance of users in selecting tags and thereby, calculate the nearest neighbors of each user and cluster them. Then, the priority of tags and items for each user is calculated for constructing a Nominal Label Matrix and Nominal Page Matrix. Finally, the fuzzy rules are generated for page recommendation. The experimentation is carried out on delicious datasets and the experimental results ensured that the proposed algorithm has achieved the maximum hit ratio of 6.6% for neighborhood size of 20, which is higher than the existing technique which obtained only 5.5%.

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Section: Review Of Related Workmentioning

confidence: 99%

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

Banda¹,

Bharadwaj²

2014

IJCIS

Self Cite

View full text Add to dashboard Cite

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E-FCM Algorithms for Collaborative Tagging System

Banda

Bharadwaj

2015

Advances in Intelligent Systems and Computing

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Recommender Systems Using Collaborative Tagging

Banda

Singh

Sơn

et al. 2020

International Journal of Data Warehousing and Mining

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Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

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Improving scalability issues in collaborative filtering based on collaborative tagging using genre interestingness measure

Cited by 3 publications

References 0 publications

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

An Approach to Enhance the Quality of Recommendation Using Collaborative Tagging

E-FCM Algorithms for Collaborative Tagging System

Recommender Systems Using Collaborative Tagging

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