• Corpus ID: 533092

NectaRSS, an RSS feed ranking system that implicitly learns user preferences

@article{Samper2006NectaRSSAR,
  title={NectaRSS, an RSS feed ranking system that implicitly learns user preferences},
  author={Juan J. Samper and Pedro A. Castillo and Lourdes Araujo and Juan Juli{\'a}n Merelo Guerv{\'o}s},
  journal={ArXiv},
  year={2006},
  volume={abs/cs/0610019}
}
In this paper a new RSS feed ranking method called NectaRSS is introduced. The system recommends information to a user based on his/her past choices. User preferences are automatically acquired, avoiding explicit feedback, and ranking is based on those preferences distilled to a user profile. NectaRSS uses the well-known vector space model for user profiles and new documents, and compares them using information-retrieval techniques, but introduces a novel method for user profile creation and… 
Intelligent rss aggregator
TLDR
This work designed and implemented an “Intelligent” RSS Aggregator that uses Adaptive Information Retrieval techniques to adapts itself to user’s interest, and proved to be a functional and user-friendly intelligent RSS aggregator.
R eview of Web Personalization
Today the modern phase of the internet is the personalize phase where the user is able to view everything that matches his/her interest and needs. Nowadays, Web users are relying totally on the
Review of Web Personalization
TLDR
The whole era of web personalization is described with a description of all the processes that have made this technique more popular and widespread and how this approach has made the internet world more facilitating and easy-to-use for the user.
The Meme Ranking Problem: Maximizing Microblogging Virality
TLDR
This paper introduces the meme ranking problem, as the problem of selecting which k memes to show to users when they log into the system, and devise a set of heuristics and compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, and with parameters learnt from real logs of meme propagations.
Meme ranking to maximize posts virality in microblogging platforms
TLDR
This paper introduces the meme ranking problem, as the problem of selecting which k memes (among the ones posted by their contacts) to show to users when they log into the system, and devise a set of heuristics and compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of meme propagations.
Social influence and its applications : an algorithmic and data mining study
TLDR
A novel data driven approach to influence models is proposed and a fresh perspective on identifying community leaders who may have high influence within their community, using a pattern mining approach is taken.
Survey on the Web Syndication Current Issues
TLDR
An overview of the challenges connected to the web syndication, like the way to produce feeds, how to store, cluster or replicate i nformation,How to query, filter index it, and to rank the results are proposed.
Influence Propagation in Social Networks: A Data Mining Perspective
  • F. Bonchi
  • Computer Science
    Web Intelligence
  • 2011
TLDR
A data mining perspective is taken and what (and how) can be learned from the available traces of past propagations of influence propagation in social networks is discussed.
Finding contexts of social influence in online social networks
TLDR
This paper poses the problem of finding contexts of social influence where the social influence is similar across all items in the context and presents a full-space clustering algorithm and a subspace clusters algorithm to find these contexts and test the algorithms on the Digg data set.
Influence Propagation in Social Networks: A Data Mining Perspective
  • F. Bonchi
  • Computer Science
    2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
  • 2011
TLDR
A data mining perspective is taken and what (and how) can be learned from the available traces of past propagations of influence propagation in social networks is discussed.
...
1
2
...

References

SHOWING 1-10 OF 13 REFERENCES
Capturing knowledge of user preferences: ontologies in recommender systems
TLDR
This work explores the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences.
WEBLOG RECOMMENDATION USING ASSOCIATION RULES
TLDR
This paper proposes automatic extraction of association rules from the results of a survey as a means to recommend to weblog readers other weblogs about related topics, based on the results a survey, and examines different support/confidence thresholds.
Ephemeral and Persistent Personalization in Adaptive Information Access to Scholarly Publications on the Web
TLDR
It is demonstrated that persistent personalization is needed and useful for information filtering systems, and ephemeral personalization leads to more effective and usable information retrieval systems.
A Web-Based E-commerce Facilitator Intermediary for Small and Medium Enterprises: A B2B/B2C Hybrid Proposal
TLDR
A web-based intermediary for e-commerce is proposed, whose main goal is to facilitate the entry of small and medium enterprises into the virtual business arena, by allowing the formation of enterprise coalitions based on the role of this intermediary, which acts as a shopping-window for their products.
Introduction to Modern Information Retrieval
TLDR
Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Electronic Commerce Recommendation Applications
  • Journal of Data Mining and Knowledge Discovery
  • 2001
Modern information retrieval
  • Modern information retrieval
  • 1999
The SMART Retrieval
  • System. Prentice-Hall,
  • 1971
The SMART Retrieval System
...
1
2
...