Description:

scholarec 1.0.1

scholarec=========Recommendation of Scholarly Works ---------------------------------[](https://travis-ci.org/arcolife/scholarec)[](https://gemnasium.com/arcolife/scholarec)[](http://dx.doi.org/10.5281/zenodo.10265)This software has been built due to a need felt for a proper recommendation system for publicly available scholarly/research works. It classifies documents and uses personalization features and content-based algorithm to suggest/recommend similar ones, possibly of interest to the user. _Note:_ Currently, full-functionality is offered by combining this package and another one, that offeres web interface (Django-based). - [arcolife/django-scholarec](https://github.com/arcolife/django-scholarec "django-scholarec")> *Inspired from an older project* [researchlei](http://cs.stanford.edu/people/karpathy/researchlei/ "BSD Licensed")*****Installation**``` gitclonehttps://github.com/arcolife/scholarec.git cd scholarec/ shsetup.sh‘‘‘∗SeeINSTALLfordetailedinstructions.∗∗Test∗∗∗Optionally,totestifinstalled,lookforadescriptiononexecuting:‘‘‘ python -m scholarec```* To see if the scripts runs without error:``` ./tests/run−tests.sh ./tests/test.py```**Usage*** To use the module in a Python script, simply import:```python import scholarec```* To check a sample run output, open log/sample_run.txt* To go for a sample run:``` $ ./tests/query_parse```Note: For developing a small database from arXiv, you need to run the query_parse script and accept "Extract PDF" option for extracting related pdf's, converting them to plain text and extracting interesting words that would later be used for recommendations and suggestions.* A simple arXiv API call can be achieved by executing the following sample code:```python import scholarecfrom scholarec.base.arxiv import DocumentArXivurl = "http://export.arxiv.org/api/query?search_query=all:%22higgs%22&start=0&max_results=2"from urllib2 import urlopenquery_xml = urlopen(url)doc = DocumentArXiv(query_xml)data_dict = doc.extract_tags()for entry_id in data_dict.keys(): print "ID: %s" % (entry_id) print(data_dict[entry_id]), "\n"``` *****FAQ**Q. What data interchange file formats have been used?A. Data conversion from XML to JSON as well as in XML itself.Q. What are the Data sources? A. Dataset currently taken from arXiv. Future: DBLP/Google Scholar. Q. How is the Data dealt with?A. ElasticSearch/MongoDB for search and storage*****LICENSE**[](http://www.gnu.org/licenses/gpl-3.0-standalone.html)

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For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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