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
Amazon.com, Inc. ( ) is an American multinational technology company focusing on e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence. It has been referred to as "one of the most influential economi ...
in 1998. It was first published in an academic conference in 2001.
Earlier collaborative filtering systems based on
rating
A rating is an evaluation or assessment of something, in terms of quality, quantity, or some combination of both.
Rating or ratings may also refer to:
Business and economics
* Credit rating, estimating the credit worthiness of an individual, c ...
similarity between users (known as
user-user collaborative filtering) had several problems:
* systems performed poorly when they had many items but comparatively few ratings
* computing similarities between all pairs of users was expensive
* user profiles changed quickly and the entire system model had to be recomputed
Item-item models resolve these problems in systems that have more users than items. Item-item models use rating distributions ''per item'', not ''per user''. With more users than items, each item tends to have more ratings than each user, so an item's average rating usually doesn't change quickly. This leads to more stable rating distributions in the model, so the model doesn't have to be rebuilt as often. When users consume and then rate an item, that item's similar items are picked from the existing system model and added to the user's recommendations.
Method
First, the system executes a model-building stage by finding the similarity between all pairs of items. This
similarity function
In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. Although no single definition of a similarity exists, usually such meas ...
can take many forms, such as correlation between ratings or cosine of those rating vectors. As in user-user systems, similarity functions can use
normalized ratings (correcting, for instance, for each user's average rating).
Second, the system executes a
recommendation stage. It uses the most similar items to a user's already-rated items to generate a list of recommendations. Usually this calculation is a
weighted sum or
linear regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is call ...
. This form of recommendation is analogous to "people who rate item X highly, like you, also tend to rate item Y highly, and you haven't rated item Y yet, so you should try it".
Results
Item-item collaborative filtering had less error than user-user collaborative filtering. In addition, its less-dynamic model was computed less often and stored in a smaller matrix, so item-item system performance was better than user-user systems.
References
Recommender systems
{{recommender systems