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 content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and 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 aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

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Research-paper recommender systems: a literature survey

In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.

https://romisatriawahono.net/lecture/rm/survey/machine%20learning/Liao%20-%20Data%20mining%20techniques%20and%20applications%20A%20decade%20review%20from%202000%20to%202011%20-%202012.pdf

Review: Data mining techniques and applications - A decade review from 2000 to 2011

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

The purpose of this paper is to discuss the emerging and innovative technologies which integrate together to form smart libraries. Smart libraries are the new generation libraries, which work with the amalgamation of smart technologies, smart users and smart services.,An extensive review of literature on “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored initially to ascertain the extent of literature published on Smart Libraries and their varied aspects. Literature was searched against various keywords like smart libraries, smart technologies, Internet of Things (IoT), Electronic resource management (ERM), Data mining, Artificial intelligence (AI), Ambient intelligence, Blockchain Technology and Augmented Reality. Later on, the works citing the literature on Smart Libraries were also explored to visualize a broad spectrum of emerging concepts about this growing trend in libraries.,The study confirms that smart libraries are becoming smarter with the emerging smart technologies, which enhances their working capabilities and satisfies the users associated with them. Implementing the smart technologies in the libraries has bridged the gap between the services offered by the libraries and the rapidly changing and competing needs of the humans.,The paper highlights the emerging smart technologies in smart libraries and how they influence the efficiency of libraries in terms of users, services and technological integration.,The paper tries to highlight the current technologies in the smart library set-ups for the efficient working of library set-ups.

Smart libraries: an emerging and innovative technological habitat of 21st century

In the past few years, we have developed a research paper recommender system for our reference management software Docear. In this paper, we introduce the architecture of the recommender system and four datasets. The architecture comprises of multiple components, e.g. for crawling PDFs, generating user models, and calculating content‐based recommendations. It supports researchers and developers in building their own research paper recommender systems, and is, to the best of our knowledge, the most comprehensive architecture that has been released in this field. The four datasets contain metadata of 9.4 million academic articles, including 1.8 million articles publicly available on the Web; the articles' citation network; anonymized information on 8,059 Docear users; information about the users' 52,202 mind‐maps and personal libraries; and details on the 308,146 recommendations that the recommender system delivered. The datasets are a unique source of information to enable, for instance, research on collaborative filtering, content‐based filtering, and the use of reference‐management and mind‐mapping software.

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The Architecture and Datasets of Docear's Research Paper Recommender System

In recent years, Recommender System's models and techniques, have been applied in e-Learning and main efforts are been centered in learners, their guide and their success when using Learning Management Systems and other Web-based and virtual artifacts. Several techniques were adapted or even developed for this purposes. Our current project is concerned to the need to develop a Recommender System Architecture that may assist teachers in their e-Learning design practices. Instructional Design Methods, Learning Theories, Efficient Searching Methods and Tools and Meta-data Managing, are examples of the necessary knowledge and abilities. In this document we are centered in presenting the overall system architecture and describing their main parts. We are also reporting actual results. Due to it's wide conception, this project involves Knowledge Engineering, Software Engineering, Machine Learning, Semantic Web Searching, and Data Mining models and tools.

A Recommender System Architecture for Instructional Engineering

Purpose – This paper aims to show the results obtained from the data mining techniques application to learning objects (LO) metadata.Design/methodology/approach – A general review of the literature was carried out. The authors gathered and pre‐processed the data, and then analyzed the results of data mining techniques applied upon the LO metadata.Findings – It is possible to extract new knowledge based on learning objects stored in repositories. For example it is possible to identify distinctive features and group learning objects according to them. Semantic relationships can also be found among the attributes that describe learning objects.Research limitations/implications – In the first section, four test repositories are included for case study. In the second section, the analysis is focused on the most complete repository from the pedagogical point of view.Originality/value – Many publications report results of analysis on repositories mainly focused on the number, evolution and growth of the learning...

Using data mining techniques for exploring learning object repositories

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

This paper presents a new evaluation approach for aontology-based expansion strategy proposed in previous work. In this work, the evaluation focuses on the search for Learning Object (LO) in specialized repositories. A total of 98 user queries onthe Gene ontology, a knowledge representation largely validated by the scientific community, were used in the test. For each one, new concepts were extracted from the ontology considering different expansion types. The work carried out demonstrates that query expansion delivers better results in terms of novelty and precision, independently of the expansion type. Also, that expansion types resulting from “part-of” relationships present better results in terms of precision and gained novelty, if compared with expansions using “is_a” relationships.

Domain Ontology-Based Query Expansion: Relationships Types-Centered Analysis Using Gene Ontology

The instructional knowledge generated by the use of a learning management system, and the search processes and the development of resources for learning, can be used to improve the environments in which instruction is designed. Our current project is concerned to the need to develop a knowledge-based architecture that may assist teachers in their e-learning design practices. Instructional design methods, learning theories, efficient searching methods and tools and metadata managing, are examples of the necessary knowledge and abilities. In this document we are centred in presenting the overall system architecture and describing their main parts. We are also reporting current results. Due to its wide conception, this project involves knowledge engineering, software engineering, machine learning, semantic web searching and data mining models and tools.

Alejandra Segura

Papers

A Recommender System Architecture for Instructional Engineering

Using data mining techniques for exploring learning object repositories

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

Domain Ontology-Based Query Expansion: Relationships Types-Centered Analysis Using Gene Ontology

Knowledge-based architecture for instructional engineering