Social computing: an intersection of recommender systems, trust/reputation systems, and social networks

Tavakolifard, Mozhgan; Almeroth, Kevin C.

doi:10.1109/mnet.2012.6246753

Cited by 50 publications

(25 citation statements)

References 7 publications

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“…3, the results of the analysis for this example are given in Sect. 4. With these results, it is shown that the convexity is strongly related to observability.…”

Section: Introductionmentioning

confidence: 74%

“…However, the main motivation of such studies is methods of implementing these systems. On the other hand, the concepts of wisdom of crowd [1], [2], crowd sourcing [3], social computing [4], and participatory sensing [5] attempt to use local information to achieve a common, centralized, global objective. Although these concepts are in the same direction with the concept discussed in this paper, there have not been any studies discussing the relationship between local information and global view in the real (physical) world.…”

Section: Introductionmentioning

confidence: 99%

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Local Information, Observable Parameters, and Global View

Saitō

2013

IEICE Trans. Commun.

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SUMMARYThe "Blind Men and an Elephant" is an old Indian story about a group of blind men who encounter an elephant and do not know what it is. This story describes the difficulties of understanding a large concept or global view based on only local information. Modern technologies enable us to easily obtain and retain local information. However, simply collecting local information does not give us a global view, as evident in this old story. This paper gives a concrete model of this story on the plane to theoretically and mathematically discuss it. It analyzes what information we can obtain from collected local information. For a convex target object modeling the elephant and a convex sensing area, it is proven that the size and perimeter length of the target object are the only parameters that can be observed by randomly deployed sensors modeling the blind men. To increase the number of observable parameters, this paper argues that nonconvex sensing areas are important and introduces composite sensor nodes as an approach to implement non-convex sensing areas. The paper also derives a model on the discrete space and analyzes it. The analysis results on the discrete space are applicable to some network related issues such as link quality estimation in a part of a network based on end-to-end probing.

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“…3, the results of the analysis for this example are given in Sect. 4. With these results, it is shown that the convexity is strongly related to observability.…”

Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Local Information, Observable Parameters, and Global View

Saitō

2013

IEICE Trans. Commun.

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“…[22], [27], [29]). Even though reputation systems share some fundamental similarities with recommender systems data [18,33], however, the simple transfer of their results to recommender systems is not possible without further ado because of the high diversity of recommender systems data.…”

Section: Research Gap: Visualization Of Recommender Systems Datamentioning

confidence: 99%

Interactive Visualization of Recommender Systems Data

Richthammer

Sänger

Pernul

2017

Proceedings of the 4th Workshop on Security in Highly Connected IT Systems

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Recommender systems provide a valuable mechanism to address the information overload problem by reducing a data set to the items that may be interesting for a particular user. While the quality of recommendations has notably improved in the recent years, the complex algorithms in use lead to high non-transparency for the end user. We propose the usage of interactive visualizations for presenting recommendations. By involving the user in the information reduction process, the quality of recommendations could be enhanced whilst keeping the system's transparency. is work gives rst insights by analyzing recommender systems data and matching them to suitable visualization and interaction techniques. e ndings are illustrated by means of an example scenario based on a typical real-world se ing.

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“…When a new node joins the system with a very low reputation score or no reputation score at all, its chance of being selected for interaction is generally rare. Hence, it is hard for a new user to raise his or her reputation score [3,10].…”

Section: Challenges In Recommender Systemmentioning

confidence: 99%

“…(2) Collaborative Filtering (CF): The recommender will retrieve items based on connections or similarities between user profiles. (3) Hybrid approach: It combines CB and CF [10].…”

Section: Introductionmentioning

confidence: 99%

Study of Collaborative Filtering Recommendation Algorithm Scalability Issue

Pagare¹,

Patil²

2013

IJCA

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Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information services. Collaborative Filtering technique is the most successful in the recommender systems field. Collaborative filtering creates suggestions for users based on their neighbors preferences. But it suffers from poor accuracy, scalability and cold start problems. The tremendous growth of the number of customers and products in recent years poses some key challenges for recommender systems in which high quality recommendations are required and more recommendations per second for millions of customers and products need to be performed. Thus, the enhancement of scalability and efficiency of collaborative filtering (CF) algorithms become progressively more important and difficult. This paper focuses on study of different collaborative filtering algorithms taking into consideration the scalability issue. The different algorithms studied are cluster based, item based and context based.

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Social computing: an intersection of recommender systems, trust/reputation systems, and social networks

Cited by 50 publications

References 7 publications

Local Information, Observable Parameters, and Global View

Local Information, Observable Parameters, and Global View

Interactive Visualization of Recommender Systems Data

Study of Collaborative Filtering Recommendation Algorithm Scalability Issue

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