Conceptual, Impact-Based Publications Recommendations

CiteSeerx is a digital library for scientific publications by computer science researchers. It also functions as a search engine with several features including autonomous citation indexing, automatic metadata extraction, full-text indexing and reference linking. Users are able to retrieve relevant documents from the CiteSeerx database directly using search queries and will further benefit if the system suggests document recommendations to the user based on their preferences and search history. Therefore, recommender systems were initially developed and continue to evolve to recommend more relevant documents to the CiteSeerx users. In this thesis, we introduce the Conceptual, Impact-Based Recommender (CIBR), a hybrid recommender system, derived from the previously implemented conceptual recommender system in CiteSeerx. The Conceptual recommender system utilized the user\u27s top weighted concepts to recommend relevant documents to the users. Our hybrid recommender system, CIBR, considers the impact factor in addition to the top weighted concepts for generating recommendations for the user. The impact factor of a document is determined by using the author\u27s h-index of the publication. A survey was conducted to evaluate the efficiency of our hybrid system and this study shows that the CIBR system generates more relevant documents as compared to those recommended by the conceptual recommender system

Recommender Systems for Digital Libraries: A review of concepts and concerns

The study hopefully has given an understanding of Recommender System (RS) concept and trends of IR systems especially in the domain of digital libraries. It unfolded the concept of RS through the review of literature and presented an outline of the concepts. Paper discussed the importance of recommender systems in the digital library domain. Study further explain the concept of different kind of RS applied to different digital library software systems. This paper shows how recommender systems functions in different library systems and how these recommender system helps to the users to find and retrieve data or information from different databases. The basic aim of this paper is to know the future aspects of recommender systems in digital library systems and the implications according to its need. This paper contains about conceptual base of the recommender systems, their approaches and their usability in different field of information gathering systems Abstract The study hopefully has given an understanding of Recommender System (RS) concept and trends of IR systems especially in the domain of digital libraries. It unfolded the concept of RS through the review of literature and presented an outline of the concepts. Paper discussed the importance of recommender systems in the digital library domain. Study further explain the concept of different kind of RS applied to different digital library software systems. This paper shows how recommender systems functions in different library systems and how these recommender system helps to the users to find and retrieve data or information from different databases. The basic aim of this paper is to know the future aspects of recommender systems in digital library systems and the implications according to its need. This paper contains about conceptual base of the recommender systems, their approaches and their usability in different field of information gathering systems

Citation recommendation: approaches and datasets

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction to automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles

Citation Recommendation: Approaches and Datasets

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction into automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods, and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.Comment: to be published in the International Journal on Digital Librarie

Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation

Contents The four CSV files are the data used for the evaluation in: Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. DOI: 10.1007/978-3-030-45439-5_15 Code: github.com/IllDepence/ecir2020 The evaluation was conducted in a citation re-prediction setting. CSV Format 7 columns divided by \u241E cited document ID for *_nomarker.csv: citation marker position ambiguous for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context adjacent cited document IDs only given in citrec_unarxive_*.csv divided by \u241F order matches 'CIT' markers in citation context citing document ID citation context MAG field of study IDs divided by \u241F predicate:argument tuples generated based on PredPatt JSON noun phrases for *_nomarker.csv: divided by \u241F for *_withmarker.csv: divided by \u241D into noun phrases noun phrase directly preceding citation marker Data Sources citrec_unarxive_cs_withmarker.csv data set unarXive Paper DOI: 10.1007/s11192-020-03382-z Data DOI: 10.5281/zenodo.2553522 filter citing doc from computer science cited doc is cited at least 5 times citrec_mag_cs_en.csv data set Microsoft Academic Graph (MAG) Paper DOI: 10.1145/2740908.2742839 filter citing doc from computer science and in English citing doc abstract in MAG given cited doc is cited at least 50 times citrec_refseer.csv data set RefSeer Paper URL: ojs.aaai.org/index.php/AAAI/article/view/9528 Data URL: psu.app.box.com/v/refseer filter for citing and cited docs title, venue, venuetype, abstract, and year not NULL citrec_acl-arc_withmarker.csv data set ACL ARC Paper URL: aclanthology.org/L08-1005 Data URL: acl-arc.comp.nus.edu.sg/ filter cited doc has a DBLP ID Paper Citation @inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15},

A research paper recommender system using a dynamic normalized tree of concepts model for user modelling

The enormous growth of information on the Internet makes finding information challenging and time consuming. Recommender systems provide a solution to this problem by automatically capturing user interests and recommending related information the user may also find interesting. In this paper, we present a novel recommender system for the research paper domain using a Dynamic Normalized Tree of Concepts (DNTC) model. Our system improves existing vector and tree of concepts models to be adaptable with a complex ontology and a large number of papers. The proposed system uses the 2012 version of the ACM Computing Classification System (CCS) ontology. This ontology has a much deeper structure than previous versions, which makes it challenging for previous ontology-based approaches to recommender systems. We performed offline evaluations using papers provided by ACM digital library for classifier training, and papers provided by CiteSeerX digital library for measuring the performance of the proposed DNTC model. Our evaluation results show that the novel DNTC model significantly outperforms the other two models: non-normalized tree of concepts and the vector of concepts models. Further, our DNTC model provides high average precision and reliable results when used in a context which the user has multiple interests and reads a large quantity of papers over time