Combining article content and Web usage for literature recommendation in digital libraries

Hwang, San‐Yih; Chuang, Shi‐Min

doi:10.1108/14684520410553750

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…Furthermore, some prior studies combine different types of information to improve the effectiveness of literature recommendation. To alleviate the low-coverage problem of usage logs, Hwang and Chuang (2004) integrate article content and usage logs to recommend articles most similar in content with recently accessed articles of an active user. To take into account the impact of academic articles for recommendation, Yang and Lin (2013) incorporate article content and citation networks using the CiteRank algorithm (Walker et al , 2007) to rank articles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Task-focused literature recommendation methods employ various types of information, such as content (Hwang and Chuang, 2004; Mobasher et al , 2000), usage logs (Hwang and Chuang, 2004; Mobasher et al , 1999, 2000), and citation networks (Yang and Lin, 2013). Specifically, given an active user’s task profile, these methods recommend articles that either have similar content or are often co-accessed (co-cited) with the articles in the given task profile.…”

Section: Introductionmentioning

confidence: 99%

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Coauthorship network-based literature recommendation with topic model

Hwang

Wei

Lee

et al. 2017

OIR

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Purpose The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network. Design/methodology/approach The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles. Findings The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method. Originality/value This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coauthorship network-based literature recommendation with topic model

Hwang

Wei

Lee

et al. 2017

OIR

View full text Add to dashboard Cite

show abstract

“…The hybrid approach, which combines content‐based and collaborative filtering approaches, has performed better (He, Kifer, Pei, Mitra, & Giles, ). Hwang and Chuang () combined article content and web usage information for literature recommendation in a digital library context. He et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A profile‐boosted research analytics framework to recommend journals for manuscripts

Silva

Yang

et al. 2014

Asso for Info Science & Tech

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With the increasing pressure on researchers to produce scientifically rigorous and relevant research, researchers need to find suitable publication outlets with the highest value and visibility for their manuscripts. Traditional approaches for discovering publication outlets mainly focus on manually matching research relevance in terms of keywords as well as comparing journal qualities, but other research-relevant information such as social connections, publication rewards, and productivity of authors are largely ignored. To assist in identifying effective publication outlets and to support effective journal recommendations for manuscripts, a threedimensional profile-boosted research analytics framework (RAF) that holistically considers relevance, connectivity, and productivity is proposed. To demonstrate the usability of the proposed framework, a prototype system was implemented using the ScholarMate research social network platform. Evaluation results show that the proposed RAF-based approach outperforms traditional recommendation techniques that can be applied to journal recommendations in terms of quality and performance. This research is the first attempt to provide an integrated framework for effective recommendation in the context of scientific item recommendation.

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“…They follow the two points: (1) articles obtained by the same query must bear some similarities in their content (2) articles appeared in the same user session also bear some degree of similarity [3]. The next year Hwang, S. Y. et al improve the method by combing the web usage mining and the article content to evaluate the similarity [4]. The weighted Association rule is exploited to analyze the transaction database to recommend related document [5].…”

Section: Relate Workmentioning

confidence: 99%

A Hybrid Method Based on HITS for Literature Recommendation

Zhang

Wang

2011

AMM

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In this paper we propose a hybrid method of literature recommendation in the academic community. First, we refer the objective recommendation based on HITS algorithm by constructing a directed graph according to the literature citation relation and then select the articles considering the authority and hub score of each article synthetically and add them to the recommendation list. This can narrow the recommendation scope and give a more authoritive recommendation. Second, the subjective recommendation is based on collaborative filtering by comparing the ratings of other similar users for the objects in recommendation list. The difference is we discover the similar user by clustering them. And the experiment shows the method can provide better recommendation results and is timesaving.

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Combining article content and Web usage for literature recommendation in digital libraries

Cited by 10 publications

References 27 publications

Coauthorship network-based literature recommendation with topic model

Coauthorship network-based literature recommendation with topic model

A profile‐boosted research analytics framework to recommend journals for manuscripts

A Hybrid Method Based on HITS for Literature Recommendation

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