Training a personal alert system for research information recommendation

Information Systems, and in particular Current Research Information Systems (CRISs), are usually quite difficult to query when looking for specific information, due to the huge amounts of data they contain. To solve this problem, we propose to use a personal search agent that uses fuzzy and rough sets to inform the user about newly available information. Additionally, in order to automate the operation of our solution and to provide it with sufficient information, a document classification module is developed and tested. This module also generates fuzzy relations between research domains that are used by the agent during the mapping process

Finding similar research papers using language models

The task of assessing the similarity of research papers is of interest in a variety of application contexts. It is a challenging task, however, as the full text of the papers is often not available, and similarity needs to be determined based on the papers' abstract, and some additional features such as authors, keywords, and journal. Our work explores the possibility of adapting language modeling techniques to this end. The basic strategy we pursue is to augment the information contained in the abstract by interpolating the corresponding language model with language models for the authors, keywords and journal of the paper. This strategy is then extended by finding topics and additionally interpolating with the resulting topic models. These topics are found using an adaptation of Latent Dirichlet Allocation (LDA), in which the keywords that were provided by the authors are used to guide the process

Metadata impact on research paper similarity

While collaborative filtering and citation analysis have been well studied for research paper recommender systems, content-based approaches typically restrict themselves to straightforward application of the vector space model. However, various types of metadata containing potentially useful information are usually available as well. Our work explores several methods to exploit this information in combination with different similarity measures

An exploratory study on content-based filtering of call for papers

Due to the increasing number of conferences, researchers need to spend more and more time browsing through the respective calls for papers (CFPs) to identify those conferences which might be of interest to them. In this paper we study several content-based techniques to filter CFPs retrieved from the web. To this end, we explore how to exploit the information available in a typical CFP: a short introductory text, topics in the scope of the conference, and the names of the people in the program committee. While the introductory text and the topics can be directly used to model the document (e.g. to derive a tf-idf weighted vector), the names of the members of the program committee can be used in several indirect ways. One strategy we pursue in particular is to take into account the papers that these people have recently written. Along similar lines, to find out the research interests of the users, and thus to decide which CFPs to select, we look at the abstracts of the papers that they have recently written. We compare and contrast a number of approaches based on the vector space model and on generative language models

Personalizing information retrieval in CRISs with Fuzzy Sets and Rough Sets

Current Research Information Systems (CRISs) usually contain large amounts of heterogeneous and distributed data, which makes finding specific information difficult for a user. It is in these cases that the concept of a personal search agent, proactively informing the user about newly available information, becomes more and more popular. But how can the agent know what is useful for the user if he has not expressed it explicitly? Our approach proposes using fuzzy and rough sets to make the matching process between the users' interests and the information in the system more flexible, as they allow expressing partial relationships and expanding queries, as well as dealing with problems like imprecision, ambiguity, or incompleteness