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Robustness of recommender systems
Proceedings of the fifth ACM conference on Recommender systems, 2011The possibility of designing user rating profiles to deliberately and maliciously manipulate the recommendation output of a collaborative filtering system was first raised in 2002. One scenario proposed was that an author, motivated to increase recommendations of his book, might create a set of false profiles that rate the book highly, in an effort to ...
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Proceedings of the sixth ACM conference on Recommender systems, 2012
A recent addition to Microsoft's Xbox Live Marketplace is a recommender system which allows users to explore both movies and games in a personalized context. The system largely relies on implicit feedback, and runs on a large scale, serving tens of millions of daily users. We describe the system design, and review the core recommendation algorithm.
Noam Koenigstein +3 more
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A recent addition to Microsoft's Xbox Live Marketplace is a recommender system which allows users to explore both movies and games in a personalized context. The system largely relies on implicit feedback, and runs on a large scale, serving tens of millions of daily users. We describe the system design, and review the core recommendation algorithm.
Noam Koenigstein +3 more
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A recommender system for metrics
Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2012Over the past 5 decades society has been increasingly requiring immediate results, and this is reflected in business. Due to this, the attention given to strategy, strategy management and strategy planning has increased. The use of the Balanced Scorecard (BSC) is a way of achieving this strategic capability. Moreover, a proper BSC has effective metrics
Luiz F. C. Tomaz +3 more
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Biases in Recommendation System
Fifteenth ACM Conference on Recommender Systems, 2021Recommendation systems shape what people consume and experience online, which makes it critical to assess their effect on society and whether they are affected by any potential source of bias. My research focuses on a specific source of bias — popularity — that is especially relevant in two online contexts: news consumption, and cultural markets ...
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Multimedia recommender systems
Proceedings of the 12th ACM Conference on Recommender Systems, 2018This tutorial introduces multimedia recommender systems (MMRS), in particular, recommender systems that leverage multimedia content to recommend different media types. In contrast to the still most frequently adopted collaborative filtering approaches, we focus on content-based MMRS and on hybrids of collaborative filtering and content-based filtering.
Yashar Deldjoo +3 more
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Asynchronous recommendation systems
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, 2007We consider the following abstraction of recommendation systems. There are n players and m objects, and each player has an arbitrary binary preference grade (“likes” or “dislikes”) for each object. The problem is that these preferences are not known, and the goal of the players is to discover their own preferences.
Baruch Awerbuch +2 more
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Information Technology & Tourism, 2010
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. Recommendation techniques can increase the usability of mobile systems providing personalized and more focussed content, hence limiting the ...
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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. Recommendation techniques can increase the usability of mobile systems providing personalized and more focussed content, hence limiting the ...
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Recommender Systems with Personality
Proceedings of the 10th ACM Conference on Recommender Systems, 2016We believe that in the future, the most common form of recommender systems will be present in a personal assistant. We claim that such an intelligent agent must be personal, i.e., know its user's preferences and recommend relevant content, a dynamic learner, instructable, supportive and affable.
Amos Azaria, Jason I. Hong
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The Review of Recommendation System
2019With the development of the Internet, the amount of information continues to increase, and the problem of “information overloading” is becoming more and more obvious. Simple information retrieval can no longer satisfies the needs of users to search for accurate information, and the recommendation system emerges.
Ning Wang, Hui Zhao, Xue Zhu, Nan Li
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Federated Recommendation Systems
2019 IEEE International Conference on Big Data (Big Data), 2019Despite its great progress so far, artificial intelligence (AI) is facing a serious challenge in the availability of high-quality Big Data. In many practical applications, data are in the form of isolated islands. Efforts to integrate the data are increasingly difficult partly due to serious concerns over user privacy and data security.
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