Difference between revisions of "Data-Driven Item Recommendation Algorithm"

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** It can be implemented by a [[Data-Driven Item Recommendations System]] (to solve a [[data-driven item recommendations task]]).
 
** It can be implemented by a [[Data-Driven Item Recommendations System]] (to solve a [[data-driven item recommendations task]]).
 
** It can range from being a [[Supervised Item Recommendations Algorithm]] to being an [[Unsupervised Item Recommendations Algorithm]].
 
** It can range from being a [[Supervised Item Recommendations Algorithm]] to being an [[Unsupervised Item Recommendations Algorithm]].
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** It can range from being a [[Simple Data-Driven Relevance-based Item Recommendation Algorithm]] to a [[Complex Data-Driven Relevance-based Item Recommendation Algorithmn]] (such as a [[sequence-aware recommendation algorithm]]).
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** It can range from being a [[Content-based Item Recommendation Algorithm]] to being an [[Interaction-based Item Recommendation Algorithm]] (to being a [[Content and Interaction-based Item Recommendation Algorithm]].
 
** It can be a [[Diversity-Enforcing Data-Driven Item Recommendation Algorithm]].
 
** It can be a [[Diversity-Enforcing Data-Driven Item Recommendation Algorithm]].
** It can range from being a [[Collaborative Filtering-based Recommendation Algorithm]] to being a [[User/Item Content-based Recommendation Algorithm]].
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** It can range from being a [[Collaborative Filtering-based Recommendation Algorithm]] to being a [[User Item Content-based Recommendation Algorithm]].
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** It can [[Adapt in Real-Time]] to [[User Request]]s.
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** It can utilize [[Long User Histori]]es ([[user history]]).
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** It can support [[Cold-Start User]] and [[Cold-Start Item]]s.
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** It can debias from historical trends to “predict the future”.
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** It can scale to large [[Item Catalog]]s.
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** It can be a [[Session-Based Recommendation Algorithm]].
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** ...
 
* <B>Example(s):</B>
 
* <B>Example(s):</B>
 
** [[Probabilistic Matrix Factorization (PMF)]]-based, which explains the latent factors of users and items from the perspective of probability based on the rating matrix.
 
** [[Probabilistic Matrix Factorization (PMF)]]-based, which explains the latent factors of users and items from the perspective of probability based on the rating matrix.
 
** [[Collaborative Deep Learning (CDL)]]-based, which performs deep learning for the items’ auxiliary information and collaborative filtering for the rating matrix.
 
** [[Collaborative Deep Learning (CDL)]]-based, which performs deep learning for the items’ auxiliary information and collaborative filtering for the rating matrix.
 
** [[additional Stacked Denoising Autoencoders(aSDAE)]], a hybrid model that combines [[SDAE]] and [[MF]], and learns the latent factors from both user-item rating matrix and auxiliary information for users and items.
 
** [[additional Stacked Denoising Autoencoders(aSDAE)]], a hybrid model that combines [[SDAE]] and [[MF]], and learns the latent factors from both user-item rating matrix and auxiliary information for users and items.
** [[Factorization Machines]]-based, ...
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** [[Factorization Machines]]-based.
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** ...
 
* <B>Counter-Example(s):</B>
 
* <B>Counter-Example(s):</B>
 
** a [[Heuristic Item Recommendation System]].
 
** a [[Heuristic Item Recommendation System]].

Revision as of 18:58, 26 March 2020

A Data-Driven Item Recommendation Algorithm is an item recommendations algorithm that is a data-driven information filtering algorithm/data-driven ranking algorithm .



References

2010