We present the design and methodology for the large scale hybrid paper recommender system used by Microsoft Academic. The system provides recommendations for approximately 160 million English research papers and patents. Our approach handles incomplete citation information while also alleviating the cold-start problem that often affects other recommender systems. We use the Microsoft Academic Graph (MAG), titles, and available abstracts of research papers to build a recommendation list for all documents, thereby combining co-citation and content based approaches. Tuning system parameters also allows for blending and prioritization of each approach which, in turn, allows us to balance paper novelty versus authority in recommendation results. We evaluate the generated recommendations via a user study of 40 participants, with over 2400 recommendation pairs graded and discuss the quality of the results using P@10 and nDCG scores. We see that there is a strong correlation between participant scores and the similarity rankings produced by our system but that additional focus needs to be put towards improving recommender precision, particularly for content based recommendations. The results of the user survey and associated analysis scripts are made available via GitHub and the recommendations produced by our system are available as part of the MAG on Azure to facilitate further research and light up novel research paper recommendation applications.
We present two possible interaction modes with the MA paper recommender platform. The first mode is via the Related Papers'' section on the MA search engine paper details page. Users can browse the MA search site using the novel semantic interpretation engine powering the search experience to view their desired paper @cite_28 . The paper details page, as seen in Fig., contains a tab for browsing related papers (with dynamic filters) that is populated using the techniques mentioned here. The second mode of interaction is via Microsoft Azure Data Lake (ADL) services. The entire MAG is published under the ODC-By open data license https: opendatacommons.org licenses by 1.0 and available for use via Microsoft Azure. Users can use scripting languages such as U-SQL and python not just to access the pre-computed paper recommendations available as part of the MAG but also to generate on-the-fly paper recommendations for arbitrary text input in a performant manner. The means by which this functionality is achieved is described in the following sections.
@cite_28
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We explore if and how Microsoft Academic (MA) could be used for bibliometric analyses. First, we examine the Academic Knowledge API (AK API), an interface to access MA data, and compare it to Google Scholar (GS). Second, we perform a comparative citation analysis of researchers by normalizing data from MA and Scopus. We find that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing. In addition, the AK API allows retrieving frequency distributions of citations. We consider these features to be a major advantage of MA over GS. However, we identify four main limitations regarding the available metadata. First, MA does not provide the document type of a publication. Second, the "fields of study" are dynamic, too specific and field hierarchies are incoherent. Third, some publications are assigned to incorrect years. Fourth, the metadata of some publications did not include all authors. Nevertheless, we show that an average-based indicator (i.e. the journal normalized citation score; JNCS) as well as a distribution-based indicator (i.e. percentile rank classes; PR classes) can be calculated with relative ease using MA. Hence, normalization of citation counts is feasible with MA. The citation analyses in MA and Scopus yield uniform results. The JNCS and the PR classes are similar in both databases, and, as a consequence, the evaluation of the researchers' publication impact is congruent in MA and Scopus. Given the fast development in the last year, we postulate that MA has the potential to be used for full-fledged bibliometric analyses. To help generate relevant suggestions for researchers, recommendation systems have started to leverage the latent interests in the publication profiles of the researchers themselves. While using such a publication citation network has been shown to enhance performance, the network is often sparse, making recommendation difficult. To alleviate this sparsity, we identify "potential citation papers" through the use of collaborative filtering. Also, as different logical sections of a paper have different significance, as a secondary contribution, we investigate which sections of papers can be leveraged to represent papers effectively. On a scholarly paper recommendation dataset, we show that recommendation accuracy significantly outperforms state-of-the-art recommendation baselines as measured by nDCG and MRR, when we discover potential citation papers using imputed similarities via collaborative filtering and represent candidate papers using both the full text and assigning more weight to the conclusion sections. We present a recommender systems approach to conference paper assignment, i.e., the task of assigning paper submissions to reviewers. We address both the modeling of reviewer-paper preferences (which can be cast as a learning problem) and the optimization of reviewing assignments to satisfy global conference criteria (which can be viewed as constraint satisfaction). Due to the paucity of preference data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn reviewer-paper preference models. Our models are evaluated not just in terms of prediction accuracy but in terms of end-assignment quality. Using a linear programming-based assignment optimization, we show how our approach better explores the space of unsupplied assignments to maximize the overall affinities of papers assigned to reviewers. We demonstrate our results on real reviewer bidding data from the IEEE ICDM 2007 conference. This dissertation investigates the role of contextual information in the automated retrieval and display of full-text documents, using robust natural language processing algorithms to automatically detect structure in and assign topic labels to texts. Many long texts are comprised of complex topic and subtopic structure, a fact ignored by existing information access methods. I present two algorithms which detect such structure, and two visual display paradigms which use the results of these algorithms to show the interactions of multiple main topics, multiple subtopics, and the relations between main topics and subtopics. The first algorithm, called TextTiling , recognizes the subtopic structure of texts as dictated by their content. It uses domain-independent lexical frequency and distribution information to partition texts into multi-paragraph passages. The results are found to correspond well to reader judgments of major subtopic boundaries. The second algorithm assigns multiple main topic labels to each text, where the labels are chosen from pre-defined, intuitive category sets; the algorithm is trained on unlabeled text. A new iconic representation, called TileBars uses TextTiles to simultaneously and compactly display query term frequency, query term distribution and relative document length. This representation provides an informative alternative to ranking long texts according to their overall similarity to a query. For example, a user can choose to view those documents that have an extended discussion of one set of terms and a brief but overlapping discussion of a second set of terms. This representation also allows for relevance feedback on patterns of term distribution. TileBars display documents only in terms of words supplied in the user query. For a given retrieved text, if the query words do not correspond to its main topics, the user cannot discern in what context the query terms were used. For example, a query on contaminants may retrieve documents whose main topics relate to nuclear power, food, or oil spills. To address this issue, I describe a graphical interface, called Cougar , that displays retrieved documents in terms of interactions among their automatically-assigned main topics, thus allowing users to familiarize themselves with the topics and terminology of a text collection.