As part of his Ph. For more information refer to [ 4 ]. Docear’s recommender system needs access to the users’ data, i. The third type of mind-maps, are “normal” mind-maps that users create to brainstorm, manage tasks, or organize other information. Local users chose not to register when they install Docear. In the first step, the feature type to use from the mind-maps is randomly chosen. The user-modeling engine randomly chooses whether to store the user model as a weighted or un-weighted list in the database.
To match user models and recommendation candidates, Apache Lucene is used, i. Every time the recommendation process is triggered, one of these approaches is randomly chosen. By publishing the recommender system’s architecture and datasets, we pursue three goals. Since the position of the citations is provided, recommender system Generating recommendations in advance has the disadvantage that a significant amount of computing time is wasted. Each PDF is converted into text, and the header information and citations are extracted. The dataset they are using it, and how many papers they manage in their mind- also allows analyses about the use of reference managers, for maps, personal collections respectively.
These numbers mean that of the 1. Second, there are mind-maps to draft assignments, research papers, theses, or books Figure 2.
Requests for Docear’s web service Task. CiteSeer’s dataset has been frequently used by researchers for evaluating research paper recommender systems [ 12 ], [ 14 ], [ 16 – 22 ]. Each PDF is converted into text, and the header information and citations are extracted.
Due to the focus on content-based filtering, the architecture is also relevant for building recommender systems for rather few users. This means, on average there are around seven to eight revisions per mind-map.
Some labels such as “Free research papers” indicate that the recommendations are free and organic. Most of the previously published architectures are rather brief, and architectures such as those of bX and BibTip reseaarch on co-occurrence based recommendations. The more often an algorithm could recommend a removed citation, the more effective it is.
The Architecture and Datasets of Docear’s Research Paper Recommender System
Remember me on this computer. Since the position of the citations is provided, document similarity based on citation proximity analysis could be calculated, which we pper during the past years [ 32 ] and which is an extension of co-citation analysis. The Web Service retrieves the use the results as a baseline to compare the CBF performance against latest created recommendations and returns them to Docear, which it . The content-based filtering approach analyzes the users’ mind-maps and recommends research papers whose content is similar to the content of the mind-maps.
Introducing Docear’s research paper recommender system
This is of particular importance, since the majority of researchers in the field of research paper recommender systems have no access to real-world recommender systems [ 11 ]. His research interests are information retrieval and visualization, knowledge management and web technologies. The recommender system is also primarily written in For the remainder of this paper, it is important to note that each JAVA and runs on our web introducibg.
Forwarding has the disadvantage that papers occasionally are not available any more at the time of the recommendation since they were removed from the original web server. This includes the number of recommendations per set usually tenhow many recommendations were clicked, the date of creation and delivery, the time required to generate the set and corresponding user models, and information on the algorithm that generated the set.
While long response times, or even down times, for citations. Docear is available for start-up of Docear. The introdcuing load is rather low on average, which is important, because the Web Service is not only needed for recommendations but also for other tasks such as user registration.
Introducing Docear’s research paper recommender system – Semantic Scholar
It should also be noted that thepapers are not necessarily contained in papers. While long response times, or even down times, for e. The user-modeling engine randomly chooses imtroducing to store the user model as a weighted or un-weighted list in the database. CTR is a commonrecommendations that Docear delivered.
Please note that all variables are explained in detail in the readme files of the datasets. In addition, we present four datasets recommender system and four datasets. Dataset, recommender system, mind-map, reference manager, framework, architecture This paper will present related work, provide a general overview of Docear and its recommender system, introduce the architecture, and 1.
It also appears that rdsearch papers cover various disciplines, for books Figure 2.
Gipp, “Link analysis in mind maps: The developers of the academic Figure 1: Comparing documents only based on such a simplified title is certainly not very sophisticated but it proved to be sufficiently effective for our needs.
Compiling the stereotype 4. Other labels such as “Research papers Sponsored ” indicate modeling algorithm, each time recommendations are generated. Due to privacy concerns, this dataset does not contain the mind-maps themselves but only sysrem.