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

Intelligent nutrition in healthcare and continuous care

In the healthcare industry, the patient's nutrition is a key factor in their treatment process. Every user has their own specific nutritional needs and requirements. An appropriate nutrition policy can therefore help the patient's recovery process and alleviate possible symptoms. Food recommender systems are platforms that offer personalised suggestions of recipes to users. However, there is a lack of usage of recipe recommendation systems in the healthcare sector. Multiple challenges in representing the domain of food and the patient's needs make it complicated to implement these systems. The present project aims to develop a platform for an intelligent planning of the user's meals, based on their clinical conditions. The application of machine learning algorithms on nutrition, in healthcare services and continuous care is thus a key topic of research. This platform will be tested and deployed at the Social Cafeteria of Vila Verde (Cantina Social da Santa Casa da Misericórdia de Vila Verde). The development of this project will use the Design Science Research (DSR) investigation methodology, ensuring that the solution to the problem accomplishes all needs and requirements of the professionals, while elucidating new knowledge both for the institution and the scientific community.FCT - Fundação para a Ciência e a Tecnologia (UID/CEC/00319/2019

A Cluster-based Recommender System

Introduction: E-commerce is growing rapidly offering a vast number of products and services to the users. Facing with a wide range of options, users cannot decide which one would be the most suitable option. Recommender systems help users to find the most suitable item easier and faster. To do this, recommender systems apply machine learning algorithms to user’s data to build sophisticated models to predict the user’s behavior in the future. There are many recommender systems employed by companies to increase their profitability. Some examples include Amazon, Movielens, Youtube, Facebook, and Linkedin. Objectives: The aim of this project is to provide a cluster-based recommender system which cluster users based on their history (previous interactions with the system) to increase the accuracy of recommendations. Method: The proposed approach consists of two phases: offline and online. In the offline phase, users are clustered using genetic algorithm. In the online phase, the appropriate cluster or clusters and neighborhood are selected for the target user. Then, his/her interesting items (not chosen yet) are determined using interesting items of his/her neighbors. Results: After implementing the proposed approach for the recommender system, it was evaluated in terms of accuracy (the portion of recommended items which have been interesting for the users) and compared it with several existing recommender systems. The results show that our approach outperforms other approaches. Conclusions: Having a good recommender system encourages users to buy new products, find new friends, or watch new videos. On the contrary, an inaccurate recommender system may discourage the users and motivates them to sign out of the system or ignore all recommendations. The approach we proposed for recommendation achieved promising results. We hope by completing the project we can use this approach in developing commercial recommender systems

Addressing Challenges of Ultra Large Scale System on Requirements Engineering

AbstractAccording to the growing evolution in complex systems and their integrations, Internet of things, communication, massive information flows and big data, a new type of systems has been raised to software engineers known as Ultra Large Scale (ULS) Systems. Hence, it requires dramatic change in all aspects of “Software Engineering” practices and their artifacts due to its unique characteristics.Attendance of all software development members is impossible to meet in regular way and face-to-face, especially stakeholders from different national and organizational cultures. In addition, huge amount of data stored, number of integrations among software components and number of hardware elements. Those obstacles constrict design, development, testing, evolution, assessment and implementation phases of current software development methods.In this respect, ULS system that's considered as a system of systems, has gained considerable reflections on system development activities, as the scale is incomparable to the traditional systems since there are thousands of different stakeholders are involved in developing software, were each of them has different interests, complex and changing needs beside there are already new services are being integrated simultaneously to the current running ULS systems.The scale of ULS systems makes a lot of challenges for Requirements Engineers (RE). As a result, the requirements engineering experts are working on some automatic tools to support requirement engineering activities to overcome many challenges.This paper points to the limitations of the current RE practices for the challenges forced by ULS nature, and focus on the contributions of several approaches to overcome these difficulties in order to tackle unsolved areas of these solutions.As a result, the current approaches for ULS miss some RE essential practices related to find vital dependent requirements, and are not capable to measure the changes impact on ULS systems or other integrated legacy systems, in addition the requirements validation are somehow depended on the user ratings without solid approval from the stakeholders

Lucene4IR: Developing information retrieval evaluation resources using Lucene

The workshop and hackathon on developing Information Retrieval Evaluation Resources using Lucene (L4IR) was held on the 8th and 9th of September, 2016 at the University of Strathclyde in Glasgow, UK and funded by the ESF Elias Network. The event featured three main elements: (i) a series of keynote and invited talks on industry, teaching and evaluation; (ii) planning, coding and hacking where a number of groups created modules and infrastructure to use Lucene to undertake TREC based evaluations; and (iii) a number of breakout groups discussing challenges, opportunities and problems in bridging the divide between academia and industry, and how we can use Lucene for teaching and learning Information Retrieval (IR). The event was composed of a mix and blend of academics, experts and students wanting to learn, share and create evaluation resources for the community. The hacking was intense and the discussions lively creating the basis of many useful tools but also raising numerous issues. It was clear that by adopting and contributing to most widely used and supported Open Source IR toolkit, there were many benefits for academics, students, researchers, developers and practitioners - providing a basis for stronger evaluation practices, increased reproducibility, more efficient knowledge transfer, greater collaboration between academia and industry, and shared teaching and training resources