Concept Based Author Recommender System for CiteSeer

The information explosion in today's electronic world has created the need for information filtering techniques that help users filter out extraneous content to identify the right information they need to make important decisions. Recommender systems are one approach to this problem, based on presenting potential items of interest to a user rather than requiring the user to go looking for them. In this paper we propose a recommender system that recommends research papers of potential interest to the author from the CiteSeer database. For each author participating in the study, we create a user profile based on their previously published papers. Based on similarities between the user profile and profiles for documents in the collection, additional papers are recommended to the author. We introduce a novel way of representing the user profiles as tree of concepts and an algorithm for computing the similarity between the user profiles and document profiles using a tree-edit distance measure. Experiments with a group of volunteers show that our tree based algorithm provides better recommendations than a traditional vector-space model based technique

When the System Becomes Your Personal Docent: Curated Book Recommendations

Curation is the act of selecting, organizing, and presenting content most often guided by professional or expert knowledge. While many popular applications have attempted to emulate this process by turning users into curators, we put an accent on a recommendation system which can leverage multiple data sources to accomplish the curation task. We introduce QBook, a recommender that acts as a personal docent by identifying and suggesting books tailored to the various preferences of each individual user. The goal of the designed system is to address several limitations often associated with recommenders in order to provide diverse and personalized book recommendations that can foster trust, effectiveness of the system, and improve the decision making process. QBook considers multiple perspectives, from analyzing user reviews, user historical data, and items\u27 metadata, to considering experts\u27 reviews and constantly evolving users\u27 preferences, to enhance the recommendation process, as well as quality and usability of the suggestions. QBook pairs each generated suggestion with an explanation that (i) showcases why a particular book was recommended and (ii) helps users decide which items, among the ones recommended, will best suit their individual interests. Empirical studies conducted using the Amazon/LibraryThing benchmark corpus demonstrate the correctness of the proposed methodology and QBook\u27s ability to outperform baseline and state-of-the-art methodologies for book recommendations

Subject-relevant Document Recommendation: A Reference Topic-Based Approach

Knowledge-intensive workers, such as academic researchers, medical professionals or patent engineers, have a demanding need of searching information relevant to their work. Content-based recommender system (CBRS) makes recommendation by analyzing similarity of textual contents between documents and users’ preferences. Although content-based filtering has been one of the promising approaches to document recommendations, it encounters the over-specialization problem. CBRS tends to recommend documents that are similar to what have been in user’s preference profile. Rationally, citations in an article represent the intellectual/affective balance of the individual interpretation in time and domain understanding. A cited article shall be associated with and may reflect the subject domain of its citing articles. Our study addresses the over-specialization problem to support the information needs of researchers. We propose a Reference Topic-based Document Recommendation (RTDR) technique, which exploits the citation information of a focal user’s preferred documents and thereby recommends documents that are relevant to the subject domain of his or her preference. Our primary evaluation results suggest the outperformance of the proposed RTDR to the benchmarks

Personalization and usage data in academic libraries : an exploratory study

Personalization is a service pattern for ensuring proactive information delivery tailored to an individual based on learned or perceived needs of the person. It is credited as a remedy for information explosion especially in the academic environment and its importance to libraries was described to the extent of justifying their existence. There have been numerous novel approaches or technical specifications forwarded for realization of personalization in libraries. However, literature shows that the implementation of the services in libraries is minimal which implies the need for a thorough analysis and discussion of issues underlying the practicality of this service in the library environment. This study was initiated by this need and it was done with the objective of finding answers for questions related to library usage data, user profiles and privacy which are among the factors determining the success of personalized services in academic libraries. With the aim of finding comprehensive answers, five distinct cases representing different approaches to academic library personalization were chosen for thorough analysis and themes extracted from them was substantiated by extensive literature review. Moreover, with the aim of getting more information, unstructured questions were presented to the libraries running the services. The overall finding shows that personalization can be realized in academic libraries but it has to address issues related to collecting and processing user/usage data, user interest management, safeguarding user privacy, library privacy laws and other important matters discovered in the course of the study.Joint Master Degree in Digital Library Learning (DILL

Application of Artificial Intelligence and Machine Learning in Libraries: A Systematic Review

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were finally selected, reviewed and analyzed to summarize on the application of AI and ML domain and techniques which are most often used in libraries. Findings show that the current state of the AI and ML research that is relevant with the LIS domain mainly focuses on theoretical works. However, some researchers also emphasized on implementation projects or case studies. This study will provide a panoramic view of AI and ML in libraries for researchers, practitioners and educators for furthering the more technology-oriented approaches, and anticipating future innovation pathways