Association rules and basics of recommendation systems

1. Recommendation Systems

2. • A recommendation system is a subclass of Information filtering
Systems that seeks to predict the rating or the preference a user
might give to an item.
• In simple words, it is an algorithm that suggests relevant items to
users.
• Eg: In the case of Netflix which movie to watch, In the case of e-
commerce which product to buy, or In the case of kindle which book
to read, etc.

4. • use-cases are
• A. Personalized Content: Helps to Improve the on-site experience by
creating dynamic recommendations for different kinds of audiences
like Netflix does.
• B. Better Product search experience: Helps to categories the product
based on their features. Eg: Material, Season, etc.

5. Reference : https://www.bluepiit.com/blog/classifying-
recommender-systems/

6. Collaborative
• In Collaborative Filtering, we tend to find similar users and
recommend what similar users like.
• In this type of recommendation system, we don’t use the
features of the item to recommend it, rather we classify the
users into clusters of similar types and recommend each user
according to the preference of its cluster.

7. • In this type of scenario, we can see that User 1 and User 2 give
nearly similar ratings to the movie, so we can conclude that
Movie 3 is also going to be averagely liked by User 1 but Movie
4 will be a good recommendation to User 2, like this we can also
see that there are users who have different choices like User 1
and User 3 are opposite to each other. One can see that User 3
and User 4 have a common interest in the movie, on that basis
we can say that Movie 4 is also going to be disliked by User 4.
This is Collaborative Filtering, we recommend to users the items
which are liked by users of similar interest domains.

8. Content based
• A Content-Based Recommender works by the data that we take
from the user, either explicitly (rating) or implicitly (clicking on a
link).
• By the data we create a user profile, which is then used to
suggest to the user, as the user provides more input or take
more actions on the recommendation, the engine becomes
more accurate.

9. • Some of the columns are blank in the matrix that is because we
don’t get the whole input from the user every time, and the goal
of a recommendation system is not to fill all the columns but to
recommend a movie to the user which he/she will prefer.
Through this table, our recommender system won’t suggest
Movie 3 to User 2, because in Movie 1 they have given
approximately the same ratings, and in Movie 3 User 1 has given
the low rating, so it is highly possible that User 2 also won’t like
it.

10. Types Of Recommendation System
• Content-Based Filtering
• Collaborative Based Filtering
• User-Based Collaborative Filtering
• Item-Based Collaborative Filtering
•

11. Evaluation Metrics
• Mean Average precision at K
• It gives how much relevant is the list of recommended items. Here precision at K
means Recommended items in top k sets that are relevant.
• Coverage
• It is the percentage of items in the training data model able to recommend in test
sets. Or Simply, the percentage of a possible recommendation system can predict.
• Personalization
• It is basically how many same items the model recommends to different users. Or,
the dissimilarity between users lists and recommendations.
• Intralist Similarity
• It is an average cosine similarity of all items in a list of recommendations.

12. Association rules
• Association rule mining finds interesting associations and
relationships among large sets of data items.
• This rule shows how frequently a itemset occurs in a transaction.
A typical example is a Market Based Analysis.
• Market Based Analysis is one of the key techniques used by
large relations to show associations between items.
• It allows retailers to identify relationships between the items
that people buy together frequently.

15. Applications
• Useful for analyzing and predicting customer behavior
• customer analytics, market basket analysis, product clustering,
catalog design and store layout.
• When diagnosing patients there are many variables to consider
as many diseases will share similar symptoms.
• Market Basket analysis, Web usage mining, continuous
production