Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access Infrastructure

Abstract

This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar documents across different digital libraries and institutional repositories with the aim to connect researchers and improve collaboration efforts. The hybrid recommender system makes use of document processing techniques, document metadata, and the similarity ranking function BM25 to provide content-based recommendations as a primary method. It also uses collaborative-filtering methods as a secondary method in a cascade hybrid recommendation technique. We also provide a real-world data feedback collection analysis for our hybrid recommender system on an academic digital repository in order to be able to identify suitable time-frames for direct feedback collection during the year.

Application of Deep Neural Networks and Human-Computer Interaction Technology in Art and Design Area

“…Computer-aided process design can only apply artificial intelligence in the early stages of development, and it is limited to intelligent thinking at this time. In practice, there are no distinct varieties of artificial intelligence [16]. Instead, a variety of applications are combined to create a more complete form [17].…”

Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System

“…In the related scientific literature, there have been several research works that explore the development of recommender systems in different domains, e.g., digital repositories [6,[8][9][10][11][12], entertainment web portals [13][14][15][16], digital education [17][18][19][20], and social settings [21][22][23][24]. In the aforementioned research studies, the algorithmic approaches that have been mainly adopted are content-based filtering, stereotypes and model-based approaches, collaborative filtering, and hybrid techniques.…”

Information Retrieval and Social Media Mining

“…These involve penalties in the range of [−1, 1] for over-expectations and under-expectations that are added to the utility score and are learned in conjunction with expectations in the same optimization process used to generate the top-N recommendations by maximizing the normalized discounted cumulative gain.Sometimes, collaborative filtering methods are combined with content-based approaches to solve some problems of the former and obtain more reliable recommendations. This combination is used in a cascade hybrid proposal for document recommendation presented in this issue [2]. A content-based method that makes use of document processing techniques and document metadata is applied first to provide an initial list of recommendations.…”

“…Sometimes, collaborative filtering methods are combined with content-based approaches to solve some problems of the former and obtain more reliable recommendations. This combination is used in a cascade hybrid proposal for document recommendation presented in this issue [2]. A content-based method that makes use of document processing techniques and document metadata is applied first to provide an initial list of recommendations.…”