2. 4R’s of
Personalization
• Recognize
• Know your customer profile including
demographics, interests and geographies.
• Remember
• Know your customer history meaning what
they buy & browse.
• Recommend
• Reach them with right content, offer and
product recommendations based upon their
actions, preferences and interests
• Relevance
• Deliver the personalization within the context
of user location, time and within the context
of digital experience.
3. Collaborative filtering
• CF recommends relevant content to users with
similar “taste”, based on their reviews /
purchases etc.
• Two Categories to capture user preferences
• Explicit Rating
• Implicit Rating
4. Nearest Neighbourhood
• The standard method of Collaborative Filtering
is known as Nearest
Neighbourhood algorithm.
• There are two types:
• User-based Collaborative Filtering
• Item-based Collaborative Filtering
5. User-based collaborative
filtering
• We have an n × m matrix of ratings, with
user uᵢ, i = 1, ...n and item pⱼ, j=1, …m.
• Now we want to predict the rating rᵢⱼ if
target user i did not watch/rate an item j.
• The process is to calculate the similarities
between target user i and all other users.
• Select the top X similar users, and take the
weighted average of ratings from these X
users with similarities as weights.
6. Item-based collaborative
filtering
• we say two items are similar when they
received similar ratings from a same user.
• Then, we will make prediction for a target user
on an item by calculating weighted average of
ratings on most X similar items from this user.
7. Matrix Factorization
• A matrix Factorization is a way of reducing a
matrix into its constituent parts.
• Example:
• LU Matrix Decomposition Technique
• A = L x U
• The factors L and U are triangular
matrices. The factorization that comes
from elimination is A = LU.
• L is the lower triangle matrix and U is the
upper triangle matrix.
8. Content Filtering
• Content-based filtering uses item features to
recommend other items similar to what the
user likes, based on their previous actions or
explicit feedback.
• For example, it can be movie attributes such
as genre, year, director, actor etc., or textual
content of articles that can extracted by
applying Natural Language Processing.
9. Hybrid Recommender
• Hybrid recommender systems combine two or
more recommendation strategies in different
ways to benefit from their complementary
advantages
10. RNN based recommender
• Recurrent Neural Networks are powerful tools
for modeling sequences. They are flexibly
extensible and can incorporate various kinds
of information including temporal order. These
properties make them well suited for
generating sequential recommendations.
11. RNN captures the sequential information present in the input data i.e. dependency between the
words in the text while making predictions:
the output (o1, o2, o3, o4) at each time step depends not only on the current word but also on the
previous words.
