A graph-based recommender system for digital library

Huang, Zan; Chung, Wingyan; Ong, Thian-Huat; Chen, Hsinchun

doi:10.1145/544229.544231

Cited by 54 publications

(61 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the user studies had five and 19 participants, respectively, which led to statistically insignificant results. Three other studies reported contradicting results for offline evaluations and user studies (two of these studies had more than 100 participants) [57,93,117]. This means that offline evaluations could not reliably predict the effectiveness in the real-world use case.…”

Section: Offline Evaluationsmentioning

confidence: 98%

“…The approaches extracted words from the title [79,85,112], abstract [26,51,61], header [43], introduction [57], foreword [57], author-provided keywords [26,57,58], and bibliography [27], as well as from the papers' body text [65,101,112]. The approaches further extracted words from external sources, such as social tags [58,62], ACM classification tree and DMOZ categories [91,94], and citation context [51,55,65].…”

Section: Content-based Filteringmentioning

confidence: 99%

“…Based on these connections, the approaches build graph networks that typically show how papers are connected through citations [10,72,84]. Sometimes, graphs include authors [4,79,127], users/customers [57], venues [10,79,127], genes and proteins [4,79], and the years the papers were published [79]. Lao et al even included terms from the papers' titles in the graph, which makes their approach a mixture of the graph and content based approach [79].…”

Section: Graph Basedmentioning

confidence: 99%

“…Lao et al even included terms from the papers' titles in the graph, which makes their approach a mixture of the graph and content based approach [79]. Depending on the entities in the graph, connections can be citations [10,79,84], purchases [57], "published in" relations, [10,79,127], authorship [4,10,127], relatedness between genes 26 [4], or occurrences of genes in papers [79]. Some authors connected entities based on non-inherent relations.…”

Section: Graph Basedmentioning

confidence: 99%

See 3 more Smart Citations

Research-paper recommender systems: a literature survey

et al. 2015

View full text Add to dashboard Cite

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of contentbased and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and Linnaeus University, Kalmar, Sweden sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspect...

show abstract

Section: Offline Evaluationsmentioning

confidence: 98%

Section: Content-based Filteringmentioning

confidence: 99%

Section: Graph Basedmentioning

confidence: 99%

Section: Graph Basedmentioning

confidence: 99%

See 2 more Smart Citations

Research-paper recommender systems: a literature survey

et al. 2015

View full text Add to dashboard Cite

show abstract

“…A limitation of this work is the use of a single test collection. As future work, we aim at evaluating our approach on a different ER setting such as, for example, graph-based tag recommendation [9].…”

mentioning

confidence: 99%

ReFER: Effective Relevance Feedback for Entity Ranking

Iofciu

Demartini

Craswell

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Web search increasingly deals with structured data about people, places and things, their attributes and relationships. In such an environment an important sub-problem is matching a user's unstructured free-text query to a set of relevant entities. For example, a user might request 'Olympic host cities'. The most challenging general problem is to find relevant entities, of the correct type and characteristics, based on a free-text query that need not conform to any single ontology or category structure. This paper presents an entity ranking relevance feedback model, based on example entities specified by the user or on pseudo feedback. It employs the Wikipedia category structure, but augments that structure with 'smooth categories' to deal with the sparseness of the raw category information. Our experiments show the effectiveness of the proposed method, whether applied as a pseudo relevance feedback method or interactively with the user in the loop.

show abstract

A graph model for E‐commerce recommender systems

Huang

Chung

Chen

2003

J. Am. Soc. Inf. Sci.

Self Cite

180

View full text Add to dashboard Cite

Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

show abstract

A graph-based recommender system for digital library

Cited by 54 publications

References 6 publications

Research-paper recommender systems: a literature survey

Research-paper recommender systems: a literature survey

ReFER: Effective Relevance Feedback for Entity Ranking

A graph model for E‐commerce recommender systems

Contact Info

Product

Resources

About