Research-paper recommender systems: a literature survey

Abstract

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...

Paper recommendation using citation proximity in bibliographic coupling

“…However, user studies have been conducted by some researchers to evaluate these [1,15,19]. We also used user studies and manual evaluation of our proposed approach.…”

“…According to a survey [1], 200 different approaches have been proposed for paper recommendation. Fifty percent of these approaches applied content-based filtering, 18% applied collaborative filtering, and 16% used graph-based techniques.…”

“…A myriad of papers continue to be published on this topic [1]. Modern researchers find it tedious to get access to the required research papers in their particular fields, due to the information overload and overabundance of publications in conferences and journals.…”

A Study on Serendipity-Oriented Music Recommendation Based on Play Information

“…좋은 추천 시스템 개발을 위해서는 사용자의 행동적 측면을 바탕으로 실질적인 선호도를 파악하고 이를 추천 아이템 선정 과정에 포함시켜야 한다 (Beel et al, 2013). 하지만 음악 아이템 의 유사성에만 근거하여 새로운 음악 아이템을 추천해주는 것 은 오히려 사용자의 만족도를 저하시킬 수 있다.…”

“…이는 내용 기 반 방식이 심리학적 음향 분석과 맥락적 내용에 대한 이해에 도움을 줄 수 있기 때문이다 (Uitdenbogerd and van Schyndel, 2002). 하지만, 앞서 지적하였듯이 사용자의 음악 아이템을 바 탕으로 연관성 높은 아이템만을 추천하는 것은 오히려 만족도 를 저하시킬 수 있으며 (Adomavicius and Tuzhilin, 2005;Beel et al, 2013;Herlocker et al, 2004;McNee et al, 2006) …”