UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

Huang, Yue; Gao, Xuedong; Gu, Shujuan

doi:10.2478/cait-2013-0043

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…In a user-based CF system [6,11,12], it first computes the similarities between profiles of all user pairs on the basis of their provided ratings or expressed interests to different items, then aggregating and evaluating the target items for the active user from other similar users, and finally recommends the top-N items to the active user based on their aggregated evaluations. In an item-based CF system [5,9,13], it first computes the similarities between characteristics of all item pairs on the basis of the ratings or interests provided by different users, then aggregating and evaluating the target items for the active user from other similar items, and finally recommends the top-N items to the active user based on their aggregated evaluations.…”

Section: Related Workmentioning

confidence: 99%

A PageRank-based collaborative filtering recommendation approach in digital libraries

Guo

Zhang

2017

Teh. vjesn.

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Original scientific paper In the current era of big data, the explosive growth of digital resources in Digital Libraries (DLs) has led to the serious information overload problem. This trend demands personalized recommendation approaches to provide DL users with digital resources specific to their individual needs. In this paper we present a personalized digital resource recommendation approach, which combines PageRank and Collaborative Filtering (CF) techniques in a unified framework for recommending right digital resources to an active user by generating and analyzing a time-aware network of both user relationships and resource relationships from historical usage data. To address the existing issues in DL deployment, including unstable user profiles, unstable digital resource features, data sparsity and cold start problem, this work adapts the personalized PageRank algorithm to rank the time-aware resource importance for more effective CF, by searching for associative links connecting both active user and his/her initially preferred resources. We further evaluate the performance of the proposed methodology through a case study relative to the traditional CF technique operating on the same historical usage data from a DL. Keywords: collaborative filtering; digital library; PageRank algorithm; recommendation approach; social networkPreporučeni pristup page-rank kolaborativnog filtriranja u digitalnim knjižnicama Izvorni znanstveni članak U sadašnje vrijeme opromnog broja podataka, eksplozivni porast digitalnih izvora u Digitalnim Knjižnicama -Digital Libraries (DLs) doveo je do ozbiljnog problema preopterećenja informacijama. Taj trend zahtijeva pristupe personaliziranih preporuka koji bi korisnike DL upoznali s digitalnim izvorima specifičnim za njihove individualne potrebe. U ovom radu predstavljamo personalizirani pristup preporuci digitalnog izvora koji kombinira tehnike PageRank i Collaborative Filtering (CF) u sjedinjenom okviru u svrhu preporuke odgovarajućih digitalnih izvora aktivnom korisniku generirajući i analizirajući mrežu u postojećem vremenu kako odnosa među korisnicima tako i odnosa među izvorima. Kako bi se obradila postojeća pitanja o postavljanju digitalnih knjižnica, uključujući nesigurne profile korisnika, nesigurna obilježja digitalnog izvora, oskudnost podataka i problem hladnog starta, ovaj rad adaptira personalizirani PageRank algoritam kako bi rangirao važnost izvora koji vodi računa o vremenu učinkovitijim CF, tražeći asocijativne linkove koji povezuju i aktivnog korisnika i njegove/njezine početno preferirane izvore. Također ocijenjujemo performansu predložene metodologije kroz analizu slučaja vezanog za tradicionalnu CF tehniku koja koristi iste podatke iz Digitalne knjižnice.Ključne riječi: digitalna knjižnica; društvena mreža; kolaborativno filtriranje; PageRank algoritam; pristup koji se preporučuje

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Section: Related Workmentioning

confidence: 99%

A PageRank-based collaborative filtering recommendation approach in digital libraries

Guo

Zhang

2017

Teh. vjesn.

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Recommendation Algorithm of Crowdfunding Platform Based on Collaborative Filtering

Wang

Zheng

et al. 2020

J. Phys.: Conf. Ser.

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With the continuous progress of Internet technology, the crowdfunding platform has become a new way of network financing. While the generated data keeps increasing, its benefit does not increase in a proportional way, resulting in the “information overload” phenomenon. The personalized recommendation system can solve this problem by mining users’ interests and preferences from a large amount of data. It has achieved success in many fields. This paper applies machine learning algorithm to build a recommendation system based on collaborative filtering. The designed personalized recommendation algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the crowdfunding platform. In addition, this paper uses the data from the crowdfunding platform in practice to complete the performance verification of the algorithm.

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Research on Consumer Psychology Modeling and Intelligent Recommendation of E-commerce Users

Huang¹,

Ying²

2021

2021 International Conference on Intelligent Transportation, Big Data &Amp; Smart City (ICITBS)

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UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

Cited by 4 publications

References 20 publications

A PageRank-based collaborative filtering recommendation approach in digital libraries

A PageRank-based collaborative filtering recommendation approach in digital libraries

Recommendation Algorithm of Crowdfunding Platform Based on Collaborative Filtering

Research on Consumer Psychology Modeling and Intelligent Recommendation of E-commerce Users

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