AI News, BOOK REVIEW: Artificial Intelligence/Search/Recommender Systems

Artificial Intelligence/Search/Recommender Systems

Recommender Systems were created to assist in sorting through the vast amount of information that the internet can provide.

on content based filtering indicates that though it a common recommender system for text based information, with current advancements it is becoming a good option for music as well.

They explain that most filter systems that deal with music data tend to create a content model of parameters from the music, which is then compared with a user’s profile also using the music parameters.

refined comparing user profiles to a content model, and reduced issues with using content based filtering with music data by introducing a learned decision tree in the user profile.

Knowledge based recommenders ask users about what they are looking for, and searches through a database to find selections based on the user’s requirements, and often ask the user to provide information as to the relevance of the choices.

created an agent based recommender which like most uses an individual’s behaviour to provide better suggestions to users within the individual’s community (group sharing similar patterns), but also extracted implicit knowledge from individual users for the groups.

The only true commonality between them is the fact that they have an input, some sort of information from the user, processing, in which relevant options are determined, and an output, usually a text recommendation that often refers to an item, website, song/artist, etc.

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Matrix Factorization for Movie Recommendations – CME 510

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RecSys 2016: Paper Session 8 - Parallel Recurrent Neural Network Architectures for Recommendations

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RecSys 2016: Paper Session 3 - Latent Factor Representations for Cold-Start Video Recommendation

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CARS and Beacons: Context-aware Recommender Systems using Indoor Localization

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