Ontology-based library recommender system using MapReduce

Chen, Lun-Chi; Kuo, Ping-Jen; Liao, I‐En

doi:10.1007/s10586-013-0342-z

Cited by 25 publications

(7 citation statements)

References 25 publications

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“…It switches between a user-based CF approach and content-based recommendation approach taking into account the digital resource's quality, to share both the user individual experience and social wisdom [17]. Chen et al [8] combined CF techniques with a personal ontology model to enhance recommendation performance in DLs by locating like-minded DL users based on ontological similarity computation.…”

Section: Related Workmentioning

confidence: 99%

“…CF has proven widely successful in some famous commercial Web-based systems including Amazon.com, Ebay.com, and Moviefinder.com to make the personalized product recommendation based on the usage data of previous decisions made for similar objectives. Some prototype DL recommender systems also use the CF technique to suggest users the digital resources specific to their individual needs (e.g., [8,21]). …”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

A PageRank-based collaborative filtering recommendation approach in digital libraries

Guo

Zhang

2017

Teh. vjesn.

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“…The trend can be found in South Korea and Asian countries as well as in the U.S. [5,6]. The fields where the service is applied become broaden into promotion and work support areas for business from general public users [7,8]. In addition, because the data established by SNS can be expressed well with graph model [9][10][11], there are many attempts to find new information in existing data by analyzing various networks [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

GRAPPE : a system for determining optimal connecting route to target person based on mutual intimacy index

Keum¹,

Nam

Kang³

et al. 2015

Cluster Comput

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Recently, the growth of social network service (SNS, Facebook) has required the search technique to utilize its distinctive characteristic which links people to people. This paper discusses the design and implementation of GRAPPE which suggests the ranked list of optimal connecting routes between two people by interaction like SNS, phone calls, texts, mails. It is based on mutual intimacy index (MII) which indicate how closely two people are related. MII is calculated periodically when a user interact with other person by smartphone. In this study, we propose a simple two-ended search method for finding optimal routes between a source person and a destination person. For the real-time service it searches optimal path within 3 step-away relationship in human network that is effective in real life while existing services in SNS usually provide one-ended search on entire paths. Furthermore, we also use the pruning technique for efficient execution time.

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“…Paper [3] proposed a personal ontology-based model for the library recommendation system. In paper [4], Li proposed a hybrid recommender system based on ontology and Web Usage Mining.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid User Profile Model for Personalized Recommender System with Linked Open Data

Luo¹,

Xu²,

Cai³

et al. 2014

2014 Enterprise Systems Conference

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In order to better enhance user experience on the web, varies applications such as search engines have integrated with recommender systems. Users will get some relevant items recommended when browsing the result page. However, building such recommendation services needs a large amount of item information, which makes it hard to start a new recommender service. Due to the development of Semantic Web and Linked Data, a vast amount of RDF data can be accessed via the Internet and naturally used as a knowledge base for recommender systems. In this paper, we study modeling user profiles in semantic environment and building a personalized recommender system exclusively on Linked Open Data. First, we define two concepts related to user browsing history and propose a hybrid user profile model (Hy-UPM). Second, we design a generic and personalized recommender system to utilize the semantic information between the items and user profile model to make recommendations. Finally, we conduct our experiment on the movie dataset of DBpedia and MovieLens. The result shows Hy-UPM has a better Mean Reciprocal Rank performance compared with other recommendation methods.

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Ontology-based library recommender system using MapReduce

Cited by 25 publications

References 25 publications

A PageRank-based collaborative filtering recommendation approach in digital libraries

A PageRank-based collaborative filtering recommendation approach in digital libraries

GRAPPE : a system for determining optimal connecting route to target person based on mutual intimacy index

A Hybrid User Profile Model for Personalized Recommender System with Linked Open Data

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