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Recommender Systems
[chapter]
2015
Re-engineering the Uptake of ICT in Schools
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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]
2021
arXiv
pre-print
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2021
Fifteenth ACM Conference on Recommender Systems
unpublished
doi:10.1145/3460231.3474616 fatcat:em4jlluu55hzlbmx3vvwaoplx4
doi:10.1145/3460231.3474616 fatcat:em4jlluu55hzlbmx3vvwaoplx4
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]
2017
Encyclopedia of Machine Learning and Data Mining
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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]
2015
Recommender Systems Handbook
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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
2011
The AI Magazine
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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
2010
Information Technology & Tourism
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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
2015
International Journal of Computer Applications
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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]
2020
arXiv
pre-print
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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
2020
IEEE Intelligent Systems
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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]
2015
Recommender Systems Handbook
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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
2004
Journal of Intelligent Information Systems
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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]
2003
arXiv
pre-print
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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
2017
Proceedings of the 19th International Conference on Enterprise Information Systems
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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
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
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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]
2021
arXiv
pre-print
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2021
Evaluation and Assessment in Software Engineering
unpublished
doi:10.1145/3463274.3463951 fatcat:swnokxx5o5aklmschqgjt43u7u
doi:10.1145/3463274.3463951 fatcat:swnokxx5o5aklmschqgjt43u7u
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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