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Item-item collaborative filtering, or item-based, or item-to-item, is a form of collaborative filtering for recommender systems based on the similarity between items calculated using people's ratings of those items. Item-item collaborative filtering was invented and used by Amazon.com in 1998.
Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features. In this system, keywords are used to describe the items, and a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to ...
Alternatively, item-based collaborative filtering (users who bought x also bought y), proceeds in an item-centric manner: Build an item-item matrix determining relationships between pairs of items; Infer the tastes of the current user by examining the matrix and matching that user's data
Content-based filtering algorithms, on the other hand, are in theory much less prone to the new item problem. Since content based recommenders choose which items to recommend based on the feature the items possess, even if no interaction for a new item exist, still its features will allow for a recommendation to be made. [7]
Then our choice function can choose the least element of every set under our unusual ordering." The problem then becomes that of constructing a well-ordering, which turns out to require the axiom of choice for its existence; every set can be well-ordered if and only if the axiom of choice holds.
Examples of binary item-based collaborative filtering include Amazon's item-to-item patented algorithm [12] which computes the cosine between binary vectors representing the purchases in a user-item matrix. Being arguably simpler than even Slope One, the Item-to-Item algorithm offers an interesting point of reference. Consider an example.
IRT is based on the idea that the probability of a correct/keyed response to an item is a mathematical function of person and item parameters. (The expression "a mathematical function of person and item parameters" is analogous to Lewin's equation , B = f(P, E) , which asserts that behavior is a function of the person in their environment.)
Knowledge-based recommender systems are often conversational, i.e., user requirements and preferences are elicited within the scope of a feedback loop.A major reason for the conversational nature of knowledge-based recommender systems is the complexity of the item domain where it is often impossible to articulate all user preferences at once.