A Fuzzy Recommender System for Public Library Catalogs

Abstract

Recommendation engines are one of the “discovery” products built into integrated library systems. These are a subclass of enterprise systems designed specifically for public and research libraries that incorporate an electronic card catalogue, circulation and inventory management, personnel and payroll systems, etc. The system vendors offer customizations for different contexts of specific library systems, but cannot create a bespoke solution for every customer. Our partner, an Edmonton‐area company, is filling this gap for a consortium of rural libraries in Alberta by creating a mobile app that interfaces with their electronic card catalog. Rural libraries are generally smaller than major urban public libraries, meaning that their holdings are limited overall, and within any given genre. This poses a severe problem for traditional collaborative‐filtering recommender algorithms, as the item sets for recommendations are limited by supply rather than by readers’ interests. The library's relatively small clientele also limits the item sets available for comparison. To deal with this ongoing “cold‐start” problem, we propose a hybridization of collaborative filtering with a content filter using a fuzzy taste vector. Experiments on two benchmark recommender data sets show that this approach is at least as accurate as existing fuzzy recommenders and is particularly effective on sparse data sets.

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

“…Collaborative filtering techniques assume a set of users that choose from a closed set of items (or actions) and explicitly or implicitly state their preferences (or ratings) for them. The items recommended to the user are the most preferable to him/her (or those with the highest predicted rating) 24 or to the group of users he/she belongs to Reference [25]. Energy saving systems that employ collaborative filtering deploy different interacting intelligent agents which dynamically capture user preferences.…”

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

“…So that, data mining can offer related books to users by analyzing lending transactions and lead to improving the quality of library resources and facilitating library management (Yi et al , 2014; Zhang, 2014). Many studies, such as Chen and Chen (2007), Jomsri (2014), Liu (2018), Morawski et al (2017), Tsuji et al (2012), Yi et al (2018), have used data mining techniques to apply recommender and intelligent systems in libraries. These systems help managers and librarians in various matters, such as recommending appropriate and relevant books to users, identify users’ behavior in returning books, clustering users on the basis of loyalty and identifying most cited books.…”

“…Recommender systems are indeed a data filtering system and personalized service provider, whose main idea is based on that a set of items available in the library recommend based on user behavioral records (Liao et al , 2010). These systems, which are content-based, collaborative-based or hybrid, predict their behavior and interest by analyzing users’ preferences (Morawski et al , 2017). Content-based systems (Chen and Chen, 2007; Mooney and Roy, 2000) are mainly based on each user’s previous preferences, collaborative filtering systems based on the preferences of similar users, and hybrid systems are a combination of both the content- and collaborative-based approaches (Morawski et al , 2017).…”

“…These systems, which are content-based, collaborative-based or hybrid, predict their behavior and interest by analyzing users’ preferences (Morawski et al , 2017). Content-based systems (Chen and Chen, 2007; Mooney and Roy, 2000) are mainly based on each user’s previous preferences, collaborative filtering systems based on the preferences of similar users, and hybrid systems are a combination of both the content- and collaborative-based approaches (Morawski et al , 2017). Therefore, this type of modern technology can solve many of the limitations of traditional methods of data analysis by predicting the details of users’ behavior and their interests (Suresh et al , 2018).…”

Fuzzy approach for production planning by using a three-dimensional printing-based ubiquitous manufacturing system

“…To consider the uncertainty, parameters, and variables in the proposed methodology, these factors are presented as fuzzy numbers. Fuzzy methods are receiving considerable attention in the field of recommendation systems (Giralt et al ., 2017; Morawski et al ., 2017). Therefore, the FUMUET system optimizes a fuzzy mixed-integer linear programming (FMILP) model and a fuzzy mixed-integer quadratic programming (FMIQP) model for conducting production and transportation planning, respectively.…”