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.[1][2] It was first published in an academic conference in 2001.[3]
Earlier collaborative filtering systems based on rating 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.
Methodedit
First, the system executes a model-building stage by finding the similarity between all pairs of items. This similarity function 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. 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".
Resultsedit
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.
Referencesedit
^"Collaborative recommendations using item-to-item similarity mappings".
^Linden, G; Smith, B; York, J (22 January 2003). "Amazon.com recommendations: item-to-item collaborative filtering". IEEE Internet Computing. 7 (1): 76–80. doi:10.1109/MIC.2003.1167344. ISSN 1089-7801. S2CID 14604122.
^Sarwar, Badrul; Karypis, George; Konstan, Joseph; Riedl, John (2001). "Item-based collaborative filtering recommendation algorithms". Proceedings of the 10th international conference on World Wide Web. ACM. pp. 285–295. CiteSeerX10.1.1.167.7612. doi:10.1145/371920.372071. ISBN978-1-58113-348-6. S2CID 8047550.
April 13, 2024
item, item, collaborative, filtering, item, based, item, item, form, collaborative, filtering, recommender, systems, based, similarity, between, items, calculated, using, people, ratings, those, items, invented, used, amazon, 1998, first, published, academic, . 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 1 2 It was first published in an academic conference in 2001 3 Earlier collaborative filtering systems based on rating 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 recomputedItem 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 editFirst the system executes a model building stage by finding the similarity between all pairs of items This similarity function 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 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 editItem 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 edit Collaborative recommendations using item to item similarity mappings Linden G Smith B York J 22 January 2003 Amazon com recommendations item to item collaborative filtering IEEE Internet Computing 7 1 76 80 doi 10 1109 MIC 2003 1167344 ISSN 1089 7801 S2CID 14604122 Sarwar Badrul Karypis George Konstan Joseph Riedl John 2001 Item based collaborative filtering recommendation algorithms Proceedings of the 10th international conference on World Wide Web ACM pp 285 295 CiteSeerX 10 1 1 167 7612 doi 10 1145 371920 372071 ISBN 978 1 58113 348 6 S2CID 8047550 Retrieved from https en wikipedia org w index php title Item item collaborative filtering amp oldid 1169195172, wikipedia, wiki, book, books, library,
