Choosing papers randomly from the top 50 results decreases the overall relevance of the delivered recommendations, yet increases the variety of recommendations, and allows for the analyzing of how relevant the search results of Lucene are at different ranks. As part of his Ph. It also appears that the papers cover various disciplines, for books Figure 2. Registered users sign-up with a username, a password, and an email address and they can use Docear’s online services. The server 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. Second, there are mind-maps to draft assignments, research papers, theses, or books Figure 2.
RELATED WORK Architectures help with the understanding and building of Several academic services published datasets, and hence have eased recommender systems, and are available in various recommendation the process of researching and developing research paper domains such as e-commerce , marketing , and engineering . Each article has a unique document id, a title, a cleantitle, on average there are around seven to eight revisions per mind-map. Local users chose not to register when they install Docear. The rating is then used to evaluate the effectiveness of categories e. Then the user is forwarded to the original URL of the recommended paper.
Every month, 3, to 4, newly created and modified mind-maps are uploaded to Docear’s server.
For instance, one . Chinese titles to be shortened to a introucing of length zero. Docear displays recommendations as publicly available research papers on the Web. Instead of indexing the original citation placeholder with , , etc.
In this paper, we presented the architecture of Sysetm research paper recommender system, and introduced four datasets containing metadata about research articles, and information about Docear’s users, their mind-maps, and the recommendations they received.
CiteULike5 and Bibsonomy6 published Datasets empower the evaluation of recommender systems by datasets containing the social tags that their users added to research enabling that researchers evaluate their systems with the same data.
The Architecture and Datasets of Docear’s Research Paper Recommender System
The recommender system is also primarily written in JAVA and runs on our web servers. Every time the recommendation process is triggered, one of these approaches is randomly chosen. The rating is then used to evaluate the effectiveness of categories e. After recommendations are content of rresearch mind-maps.
Introducing Docear’s research paper recommender system
Information on the latter ones is provided in the mind-map dataset. Second, there are mind-maps to draft assignments, research papers, theses, or books Figure 2.
For the first step, the feature type to use from the mind-maps is randomly each user, the label is randomly docearrs, when the user registers. Some mind-maps are uploaded for backup purposes, but most mind-maps are uploaded as part of the recommendation process.
Introducing Docear’s research paper recommender system – Semantic Scholar
CTR is a common performance measure in online advertisement and recommender systems evaluation and allows for the analyzing of the effectiveness of recommendation algorithms. Screenshot of a research paper draft in Docear. He is interested in literature recommender systems, search engines and introeucing computer interaction. For instance, one randomly arranged algorithm might utilize the one hundred resexrch recently created citations in the user’s mind-maps, weight the citations with CC-IDF, and store the five highest weighted citations as a user model.
There are three types of users in Docear, namely registered users, local users, and anonymous users. Every five minutes — or when Docear starts — Docear sends all mind-maps located in the Table 1: In addition, only those libraries having at least 20 articles were included in the dataset.
These papers are recommended with the stereotype approach, which is later explained in detail. 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.
In addition, users may explicitly request recommendations at any time.
RELATED WORK Architectures help with the understanding and building of Several academic services published datasets, and hence have eased recommender systems, and are available in various recommendation the process of researching and developing research paper domains sytsem as e-commerce , marketing , and engineering .
These papers are about academic writing and search, i. Most of the previously published architectures are rather brief, and architectures such as those of bX and BibTip focus on co-occurrence based recommendations.
Other labels such as “Research papers Sponsored ” indicate modeling algorithm, each time recommendations are generated. The feature type may be terms, citations, or both. From Lucene’s top 50 search results, a set of ten papers is randomly selected as recommendations.
It is assumed that if an algorithm could recommend the removed citation, the algorithm was effective. For each node, technical details.
The developers of the academic Figure 1: Due to privacy concerns, this dataset does not contain the mind-maps 19 This is a very rough estimate, as we did not keep track of the exact working themselves but only inroducing.
The Ubiquitous Information Management and Communication mind-map dataset is smaller than the dataset e.