Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system

Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies

Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).Recommendation Strategy; Simulation Study; Way-Finding; Collaborative Filtering; Rating

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity