Abstract: Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite the wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This paper proposes a highly accurate hotel recommender system, implemented in various layers. Using multi-aspect rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing and topic modelling techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system.
Abstract: In spite of the global efforts toward gender equality, female researchers are still underrepresented in professional scientific activities. The gender gap is more seen in engineering and math-intensive technological scientific fields thus calling for a specific attention. This paper focuses on the Canadian funded researchers who are active in natural sciences and engineering, and analyses the gender aspects of researchers’ performance, their scientific collaboration patterns as well as their share of the federal funding within the period of 2000 to 2010. Our results confirm the existence of gender disparity among the examined Canadian researchers. Although it was observed that male researchers have been performing better in terms of number of publications, the impact of the research was almost the same for both genders. In addition, it was observed that research funding is more biased towards male researchers and they have more control over their scientific community as well.
Abstract: Every year, a considerable amount of money is being
invested on research, mainly in the form of funding allocated to
universities and research institutes. To better distribute the available
funds and to set the most proper R&D investment strategies for the
future, evaluation of the productivity of the funded researchers and
the impact of such funding is crucial. In this paper, using the data on
15 years of journal publications of the NSERC (Natural Sciences and
Engineering research Council of Canada) funded researchers and by
means of bibliometric analysis, the scientific development of the
funded researchers and their scientific collaboration patterns will be
investigated in the period of 1996-2010. According to the results it
seems that there is a positive relation between the average level of
funding and quantity and quality of the scientific output. In addition,
whenever funding allocated to the researchers has increased, the
number of co-authors per paper has also augmented. Hence, the
increase in the level of funding may enable researchers to get
involved in larger projects and/or scientific teams and increase their
scientific output respectively.