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Recommender Systems [chapter]

Luis Anido-Rifón, Juan Santos-Gago, Manuel Caeiro-Rodríguez, Manuel Fernández-Iglesias, Rubén Míguez-Pérez, Agustin Cañas-Rodríguez, Victor Alonso-Rorís, Javier García-Alonso, Roberto Pérez-Rodríguez, Miguel Gómez-Carballa, Marcos Mouriño-García, Mario Manso-Vázquez (+1 others)
2015 Re-engineering the Uptake of ICT in Schools  
The recommender is based on an ontology that was developed in a collaborative way by a multi-disciplinary team of experts.  ...  Selecting the more suitable educational resources to create learning activities in the classroom may be a challenging task for teachers in primary and secondary education because of the large amount of  ...  According to this definition, we might think of content or knowledge based systems; nevertheless, semantic technologies are also used for collaborative recommender systems (e.g.  ... 
doi:10.1007/978-3-319-19366-3_6 fatcat:4r3anvh4lrddfdvcw7smsn4ypu

Scaling Enterprise Recommender Systems for Decentralization [article]

Maurits van der Goes
2021 arXiv   pre-print
Creating a culture of self-service, automation, and collaboration to scale recommender systems for decentralization.  ...  We demonstrate a practical use case of a self-service ML workspace deployment and a recommender system, that scale faster to subsidiaries and with less technical debt.  ...  We learned that a culture with self-service, automation, and collaboration is key to enable an enterprise to scale recommender systems with shared ownership.  ... 
arXiv:2109.09231v1 fatcat:lg6op2hs3jgk3mvawam7kwaniy

Recommender Systems [chapter]

Prem Melville, Vikas Sindhwani
2017 Encyclopedia of Machine Learning and Data Mining  
The myriad approaches to recommender systems can be broadly categorized as: • Collaborative Filtering (CF): In CF systems, a user is recommended items based on the past ratings of all users collectively  ...  classifiers (Foster and Tom 2001).  ...  Learning Individual Rules Conceptually, rule learning may be viewed as a search in the space of possible rules.  ... 
doi:10.1007/978-1-4899-7687-1_964 fatcat:3voghk7xz5cindlgj4pwek7r6u

Panorama of Recommender Systems to Support Learning [chapter]

Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis
2015 Recommender Systems Handbook  
In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework.  ...  This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000-2014).  ...  Related to this, [RS45-2010] identified the advantages of using a discussion forum within an e-learning system to foster communication between learners [1] and MASSAYO [RS62-2012] suggested that recommendations  ... 
doi:10.1007/978-1-4899-7637-6_12 fatcat:fdi74mokhnhuhfqijgwmucgbny

Recommender Systems in Requirements Engineering

Bamshad Mobasher, Jane Cleland-Huang
2011 The AI Magazine  
As a result, many human intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine learning techniques  ...  In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements  ...  Recommender systems have been used to support each of these individual tasks.  ... 
doi:10.1609/aimag.v32i3.2366 fatcat:xwdfaw6r7vfhnhzxyhylclbvta

Mobile Recommender Systems

Francesco Ricci
2010 Information Technology & Tourism  
Mobile phones are becoming a primary platform for information access and when coupled with recommender systems technologies they can become key tools for mobile users both for leisure and business applications  ...  In this paper we review the major issues and opportunities that the mobile scenario opens to the application of recommender systems especially in the area of travel and tourism.  ...  To build the recommendation list, the system makes use of patterns of co-occurrence of positions and activities, and employs collaborative filtering technology.  ... 
doi:10.3727/109830511x12978702284390 fatcat:5236mvpazzf3hnb2urwaqux22a

Socially-Aware Recommender Systems

Nana YawAsabere, Wisdom Kwawu Torgby, Tonny Montana Adegboyega, Dorothy Anima Frempong
2015 International Journal of Computer Applications  
Are these social properties relevant in a recommendation process? Can social properties such as social ties and degree centrality of users be applied to generate effective recommendations?  ...  The authors envisage that innovative research in recommender systems should integrate the social properties of users in order to generate trustworthy and efficient recommendations.  ...  There are two main traditional approaches that are used to design a recommender system for social networks, these are Collaborative Filtering (CF) and Content-Based Filtering (CBF).  ... 
doi:10.5120/20134-2224 fatcat:zahvhaz22neplo5ixawjq7bpcq

Respect for Human Autonomy in Recommender Systems [article]

Lav R. Varshney
2020 arXiv   pre-print
In this position paper, we argue that there is a need to specifically operationalize respect for human autonomy in the context of recommender systems.  ...  In this sense, recommender systems may be deleterious to notions of human autonomy.  ...  These impacts on human attention and behavior may be perceived in different manners at the individual level and at the population level: although recommender systems may push individuals into considering  ... 
arXiv:2009.02603v1 fatcat:s23glw3nwrdg7mwluhcggzcpw4

Differentially Private Collaborative Coupling Learning for Recommender Systems

Yanjun Zhang, Guangdong Bai, Mingyang Zhong, Xue Li, Ryan Ko
2020 IEEE Intelligent Systems  
Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data.  ...  In this work, we develop a distributed collaborative coupling learning system which enables differential privacy.  ...  With such enrichment (in useruser and user-item couplings as well), a better recommendation quality in individual platforms can be achieved .  ... 
doi:10.1109/mis.2020.3005930 fatcat:n6yr7wfidrdqvc27magihzokni

Privacy Aspects of Recommender Systems [chapter]

Arik Friedman, Bart P. Knijnenburg, Kris Vanhecke, Luc Martens, Shlomo Berkovsky
2015 Recommender Systems Handbook  
Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their  ...  We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of the recommenders.  ...  [129] apply a kNN algorithm and a random forest algorithm to learn users privacy preferences in a location-sharing system.  ... 
doi:10.1007/978-1-4899-7637-6_19 fatcat:a37glurl2vgyhconf6ihghekge

Recommender Systems Research: A Connection-Centric Survey

Saverio Perugini, Marcos André Gonçalves, Edward A. Fox
2004 Journal of Intelligent Information Systems  
Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences.  ...  Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective.  ...  We thank Naren Ramakrishnan for planting the idea for this survey during his Spring 2001 offering of CS6604: Recommender Systems at Virginia Tech.  ... 
doi:10.1023/b:jiis.0000039532.05533.99 fatcat:rscxs4oypfffpkvkbntzz4xnx4

A Connection-Centric Survey of Recommender Systems Research [article]

Saverio Perugini, Marcos Andre Goncalves, Edward A. Fox
2003 arXiv   pre-print
Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences.  ...  Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective.  ...  We thank Naren Ramakrishnan for planting the idea for this survey during his Spring 2001 offering of CS6604: Recommender Systems at Virginia Tech.  ... 
arXiv:cs/0205059v2 fatcat:t45q7wvdmrg3badt2ioeagwasa

Incorporating Situation Awareness into Recommender Systems

Jeremias Dötterl, Ralf Bruns, Jürgen Dunkel
2017 Proceedings of the 19th International Conference on Enterprise Information Systems  
Situation-aware recommender systems adapt to changes in the user's environment and therefore are able to offer recommendations that are more appropriate for the current situation.  ...  The proposed system considers both (semi-)static user profiles and volatile situational knowledge to obtain meaningful recommendations.  ...  CEP is a software technology to process a stream of events in real-time.  ... 
doi:10.5220/0006385106760683 dblp:conf/iceis/DotterlBD17 fatcat:bvhzs4qhz5ainmgruqbn5roqia

Moving beyond linearity and independence in top-N recommender systems

Evangelia Christakopoulou
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
This paper suggests a number of research directions in which the recommender systems can improve their quality, by moving beyond the assumptions of linearity and independence that are traditionally made  ...  More specifically, we focus on the development of methods capturing higher-order relations between the items, cross-feature interactions and intra-set dependencies which can potentially lead to a considerable  ...  Request We think that these assumptions while providing us with easy-to-interpret and simple models, they set a limit to the capabilities of recommender systems.  ... 
doi:10.1145/2645710.2653361 dblp:conf/recsys/Christakopoulou14 fatcat:ananvm7tvver7b2t6wxk6rmfb4

Recommender Systems for Software Project Managers [article]

Liang Wei, Luiz Fernando Capretz
2021 arXiv   pre-print
This experiment examines four open source recommender systems and implemented a customized recommender engine with two industrial-oriented packages: Lenskit and Mahout.  ...  The design of recommendation systems is based on complex information processing and big data interaction.  ...  By creating a user-based recommender, system will compute recommendations for a particular user to look for others with a similar taste and pick the recommendations from their items.  ... 
arXiv:2108.04311v1 fatcat:kwxlipm7cbbwdm7qdvgfdudjqu
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