Data Analytics - Papers - Shmueli et al. 2016 - - Association Rules and Collaborative Filtering In Data Mining for Business Analytics
9 important questions on Data Analytics - Papers - Shmueli et al. 2016 - - Association Rules and Collaborative Filtering In Data Mining for Business Analytics
What is 'Item Based Collaborative Filtering'?
What is 'Association Rule Discovery'?
What are two shortcoming of Association Rules?
- The profusion/abundanc of rules that are generated
- Rare combinations tend to be ignored because they do not meet the minimum support requirement
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How can you overcome the shortcoming of not meeting the minimum support requirement of Association Rules?
What is the main advantage of Association Rules?
It is therefore Transparant and Easy to Understand.
What are the two stages of the process creating an Association Rule
- A set of candidate rules bases on frequent itemsets is generated
- From these rules, the rules that indicate the strongest association between items are selected
The user also indicates a minimum support and confidence values, and the Lift Ratio is used to compare the efficiency of the rule. (by detecting a real association and comparing this to a random combination)
Explain the difference between the use of the association rule and item based collaborative filtering with an example;
If the rule is "IF milk, THEN cookies and cornflakes," then the association rules would recommend
cookies and cornflakes to a milk purchaser, while item-based collaborative filtering would recommend
the most popular single item purchased with milk.
What is an important component for success of collaborative filtering?
What are two disadvantages of Collaborative Filtering that are mentioned in the paper?
- They cannot generate recommendations for new users or new items
- User based Collaborative Filtering becomes computationally challenging with huge numbers of users - alternatives such as items-based methods or dimension reduction are then preffered
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