What are the two types of filtering?
- Content-Based
- Collaborative
Content-Based Filtering: Idea
recommends items based on their features and the user’s preferences for those features
Content-Based Filtering: on what does it rely?
on a profile of the user’s prefferneces
Content-Based Filtering: Process
- build a profile for each user based on their preferences for different features of items
- recommend items that match the user’s profile and have features similar to what they have liked in the past
Content-Based Filtering: Pros
- addresses the cold start problem for new items
- can provide explanations fo recommendations based on item features
Content-Based Filtering: Cons
- may not capture complex user preferences that go beyond item features
- requires detailed information about the items and user preferences for features
Collaborative Filtering: Idea
makes recommendations based on user behavior and preferences
What does Collaborative Filtering assume?
that users who have agreed in the past tend to agree in the future
What are the types of Collaborative Filtering?
- User-Based
- Item-Based
How does User-Based Collabroative Filtering recommend items?
based on the preferences of users who are similar to the target user
How does Item-Based Collaborative Filtering recommend items?
it recommends items that are similar to those liked by the target user
Collaborative Filtering: Pros
- does not require knowledge of item features
- can capture complex patterns and user preferences
Collaborative Filtering: Cons
- cold start problem
- sparsity
Define the cold start problem
it can be challenging for Collaborative Filtering to provide accurate recommendations for new users or items with little or no history
Define Sparsity
Collaborative Filtering: when dealing with a large number of users and items, the user-item interaction matrix can be sparse, making it difficult to find similar users or items
Define User based collaborative filtering
similar tastes in the past
will have similar tastes in the future
Formula pred(Alice,Item5)
Formula sim(Item5, Item4)
Commonly used techniques of content-based filtering
- TF-IDF
- Clustering
- Decision trees
- ANN
- Bayesian networks
Well known problems of content-based filtering
- Cold-start problem for new users
- Cold-start problem for new items
- Scalability
- Sparsity problem
- Exotic profiles
Chatbot definition
computerized service that enables easy conversations between humans and humanlike computerized robots
Process of chatting with bots (graph)
Chatbots: categories
- enterprise chatbots
- virtual personal assistants
- Alexda
- Apple Siri
- Cortana - Robo-advisors
- financial advisor
- shopping advisor
- travel
- medical advisor
Chatbot: IBM estimates
265 billion service requests every year, addressing them costs companies 1.3 trillion dollars
