Multidimensional Fairness in Paper Recommendation

To prevent potential bias in the paper review and selection process for conferences and journals, most include double blind review. Despite this, studies show that bias still exists. Recommendation algorithms for paper review also may have implicit bias. We offer three fair methods that specifically take into account author diversity in paper recommendation to address this. Our methods provide fair outcomes across many protected variables concurrently, in contrast to typical fair algorithms that only use one protected variable. Five demographic characteristics-gender, ethnicity, career stage, university rank, and geolocation-are included in our multidimensional author profiles. The Overall Diversity approach uses a score for overall diversity to rank publications. The Round Robin Diversity technique chooses papers from authors who are members of each protected group in turn, whereas the Multifaceted Diversity method chooses papers that initially fill the demographic feature with the highest importance. We compare the effectiveness of author diversity profiles based on Boolean and continuous-valued features. By selecting papers from a pool of SIGCHI 2017, DIS 2017, and IUI 2017 papers, we recommend papers for SIGCHI 2017 and evaluate these algorithms using the user profiles. We contrast the papers that were recommended with those that were selected by the conference. We find that utilizing profiles with either Boolean or continuous feature values, all three techniques boost diversity while just slightly decreasing utility or not decreasing. By choosing authors who are 42.50% more diverse and with a 2.45% boost in utility, our best technique, Multifaceted Diversity, suggests a set of papers that match demographic parity. The selection of grant proposals, conference papers, journal articles, and other academic duties might all use this strategy.Comment: 22 pages, Preprint of paper in Springer boo

An Intelligent Information Retrieval System Using Automatic Word Sense Disambiguation

This is the published version. Copyright De GruyterThis paper aims to establish that an intelligent contextual infonnation retrieval (IR) system can improve the quality of search results by retrieving more relevant results than those obtained with traditional search engines. Search engines capable of implicit, explicit, and no contextual retrieval were designed and implemented and their performances studied. Experimental results showed that search engines with contextual IR produce results that are more relevant, and the outcomes further indicate that there is no perceived gain in choosing specifically any one of the two approaches of implicit or explicit. The performance of the indexing mechanism, as it classifies document tokens with their appropriate contexts/word sense, was evaluated. The effectiveness of the word sense disambiguation process was found to depend to a great extent on the process (implementation) as well as the raw data (thesaurus)

Search improvement via automatic query reformulation

Users of online retrieval systems experience many difficulties, particularly with search tactics. User studies have indicated that searchers use vocabulary incorrectly and do not take full advantage of iteration to improve their queries. To address these problems, an expert system for online search assistance was developed. This prototype augments the searching capabilities of novice users by providing automatic query reformulation to improve the search results, and automatic ranking of the retrieved passages to speed the identification of relevant information. Users' search performance using the expert system was compared with their search performance on their own, and their search performance using an online thesaurus. The following conclusions were reached: 1) The expert system significantly reduced the number of queries necessary to find relevant passages compared with the user searching alone or with the thesaurus. 2) The expert system produced marginally significant improvements in precision compared with the user searching on their own. There was no significant difference in the recall achieved by the three system configurations. 3) Overall, the expert system ranked relevant passages above irrelevant passages

A Framework for Interaction-driven User Modeling of Mobile News Reading Behaviour

The news you read is, of course, a highly individual choice and one for which substantial and successful news recommendation techniques have been developed. But as well as what news you read, the way you choose and read that news is also known to be highly individual. We propose a framework for extending the user profile of news readers with features of these interactions. The extensions are dynamic through monitoring an individual's reading and browsing activity. They include factors learned from the user's interaction log and also factors inferred from category level definitions contained in the framework. We report a study in which users' interaction logs with a news app are used to generate user profiles that are verified with self-reported questionnaire data about reading habits. We discuss the implications of our user modeling approach in news personalisation for both recommendation and user interface personalisation for news apps