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Antara bukti kekuasaan Allah yang dapat kita Lihat Dengar Hidu Rasa

Definition of Collaborative Filtering

Collaborative filtering (CF) is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets.

Collaborative filtering methods have been applied to many different kinds of data including sensing and monitoring data – such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data – such as financial service institutions that integrate many financial sources; or in electronic commerce and web 2.0 applications where the focus is on user data, etc.

Collaborative filtering (CF) is a common Web technique for generating personalized recommendations. Examples of its use include Amazon, iTunes, Netflix, LastFM, StumbleUpon, andDelicious. Abbreviated as CF, in electronic commerce it is the method and process used to match data of one user with data for similar users, based on purchase and browsing patterns. Collaborative filtering allows merchants to provide customers with future purchase recommendations.

Sources: Wikipedia and webwhompers.com

Illustration of Collaborative filtering concept:

Definition of Learning 3.0

According to The American Society of Training & Development (ASTD), the meaning of learning 3.0:

“A range of Internet-based services and technologies that include components such as natural language search, forms of artificial intelligence and machine learning, software agents that make recommendations to users, and the application of context to content. By making data more understandable to machines, it also makes information easier to find and more understandable to people. Ultimately, it makes data integration and access easier, helping to usher in an era of seamless connectivity to a smarter Web, regardless of device.”

Factors of collaborative filtering

Collaborative filtering with ensembles

Collaborative Filtering involves filtering and users:

• Working together to share reactions to information
• Looking for masses of patterns
• Thrives on masses of data
• Leverages mass of user intelligence (more users = smarter)

Collaborative Filtering naturally follows from combining various aspects of each of these fields. Their

• How Information is used by individuals and (intelligent) organizations
• Filtering
• Computer Supported Cooperative Work
• Agent

Filtering methods:

• Boolean searches
• Keywords (single word and aggregate for entire documents)
• Vector matching
• Probablistic (statistical) models
• Log files
• Selection
• Combinations of methods

Indicators of Collaborative Filtering

Indicators of active participation, which include the number of messages sent by individual participants, the number of documents uploaded, the number of chat sessions attended, etc;

Indicators of passive participation, which include the number of messages read, the number of documents downloaded, etc; Indicators of continuity, that is the distribution of participation along time

Here is the breadth of a user model domain:

• User (casual, research, schedules)
• Sources (rarity, frequency) – stock market quotes, news, technical reports / broadcast, narrowcast
• Filters (source, keyword, other indicators)
• System (combinations)
• System profile (updated, change per usage characteristics (i.e. holidays)Definition of Collaborative Filtering

  Collaborative filtering (CF) is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets.

  Collaborative filtering methods have been applied to many different kinds of data including sensing and monitoring data – such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data – such as financial service institutions that integrate many financial sources; or in electronic commerce and web 2.0 applications where the focus is on user data, etc.

  Collaborative filtering (CF) is a common Web technique for generating personalized recommendations. Examples of its use include Amazon, iTunes, Netflix, LastFM, StumbleUpon, andDelicious. Abbreviated as CF, in electronic commerce it is the method and process used to match data of one user with data for similar users, based on purchase and browsing patterns. Collaborative filtering allows merchants to provide customers with future purchase recommendations.

  Sources: Wikipedia and webwhompers.com

  Illustration of Collaborative filtering concept:

  Definition of Learning 3.0

  According to The American Society of Training & Development (ASTD), the meaning of learning 3.0:

  “A range of Internet-based services and technologies that include components such as natural language search, forms of artificial intelligence and machine learning, software agents that make recommendations to users, and the application of context to content. By making data more understandable to machines, it also makes information easier to find and more understandable to people. Ultimately, it makes data integration and access easier, helping to usher in an era of seamless connectivity to a smarter Web, regardless of device.”

   

   

  Factors of collaborative filtering

  Collaborative filtering with ensembles

  Collaborative Filtering involves filtering and users:

  • Working together to share reactions to information
  • Looking for masses of patterns
  • Thrives on masses of data
  • Leverages mass of user intelligence (more users = smarter)

  Collaborative Filtering naturally follows from combining various aspects of each of these fields. Their

  • How Information is used by individuals and (intelligent) organizations
  • Filtering
  • Computer Supported Cooperative Work
  • Agent

  Filtering methods:

  • Boolean searches
  • Keywords (single word and aggregate for entire documents)
  • Vector matching
  • Probablistic (statistical) models
  • Log files
  • Selection
  • Combinations of methods

  Indicators of Collaborative Filtering

  Indicators of active participation, which include the number of messages sent by individual participants, the number of documents uploaded, the number of chat sessions attended, etc;

  Indicators of passive participation, which include the number of messages read, the number of documents downloaded, etc; Indicators of continuity, that is the distribution of participation along time

  Here is the breadth of a user model domain:

  • User (casual, research, schedules)
  • Sources (rarity, frequency) – stock market quotes, news, technical reports / broadcast, narrowcast
  • Filters (source, keyword, other indicators)
  • System (combinations)
  • System profile (updated, change per usage characteristics (i.e. holidays)

INOVASI DALAM PNP PENDIDIKAN ISLAM

collaborative Filtering for Teaching in a Laerning 3.0 Environment

Click to access Maria.pdf

http://ir.library.oregonstate.edu/xmlui/bitstream/handle/1957/69/rinn177-191.pdf?sequence=1

http://www.allacademic.com/meta/p_mla_apa_research_citation/0/3/6/1/6/p36164_index.html

http://www.webopedia.com/TERM/C/collaborative_filtering.html

http://en.wikipedia.org/wiki/Collaborative_filtering

PENGERTIAN

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