Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation

Contents The four CSV files are the data used for the evaluation in: Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. DOI: 10.1007/978-3-030-45439-5_15 Code: github.com/IllDepence/ecir2020 The evaluation was conducted in a citation re-prediction setting. CSV Format 7 columns divided by \u241E cited document ID for *_nomarker.csv: citation marker position ambiguous for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context adjacent cited document IDs only given in citrec_unarxive_*.csv divided by \u241F order matches 'CIT' markers in citation context citing document ID citation context MAG field of study IDs divided by \u241F predicate:argument tuples generated based on PredPatt JSON noun phrases for *_nomarker.csv: divided by \u241F for *_withmarker.csv: divided by \u241D into noun phrases noun phrase directly preceding citation marker Data Sources citrec_unarxive_cs_withmarker.csv data set unarXive Paper DOI: 10.1007/s11192-020-03382-z Data DOI: 10.5281/zenodo.2553522 filter citing doc from computer science cited doc is cited at least 5 times citrec_mag_cs_en.csv data set Microsoft Academic Graph (MAG) Paper DOI: 10.1145/2740908.2742839 filter citing doc from computer science and in English citing doc abstract in MAG given cited doc is cited at least 50 times citrec_refseer.csv data set RefSeer Paper URL: ojs.aaai.org/index.php/AAAI/article/view/9528 Data URL: psu.app.box.com/v/refseer filter for citing and cited docs title, venue, venuetype, abstract, and year not NULL citrec_acl-arc_withmarker.csv data set ACL ARC Paper URL: aclanthology.org/L08-1005 Data URL: acl-arc.comp.nus.edu.sg/ filter cited doc has a DBLP ID Paper Citation @inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15},

Extracting, mining and predicting users’ interests from social media

The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users’ interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users’ interests and preferences. In this monograph, we will cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and opportunities for further work

Exploring the Potential of User Modeling Based on Mind Maps

Mind maps have not received much attention in the user modeling and recommender system community, although mind maps contain rich information that could be valuable for user-modeling and recommender systems. In this paper, we explored the effectiveness of standard user-modeling approaches applied to mind maps. Additionally, we develop novel user modeling approaches that consider the unique characteristics of mind maps. The approaches are applied and evaluated using our mind mapping and reference-management software Docear. Docear displayed 430,893 research paper recommendations, based on 4,700 user mind maps, from March 2013 to August 2014. The evaluation shows that standard user modeling approaches are reasonably effective when applied to mind maps, with click-through rates (CTR) between 1.16% and 3.92%. However, when adjusting user modeling to the unique characteristics of mind maps, a higher CTR of 7.20% could be achieved. A user study confirmed the high effectiveness of the mind map specific approach with an average rating of 3.23 (out of 5), compared to a rating of 2.53 for the best baseline. Our research shows that mind map-specific user modeling has a high potential, and we hope that our results initiate a discussion that encourages researchers to pursue research in this field and developers to integrate recommender systems into their mind mapping tools