NMLS - 4. Recommender Systems - Content Based Filtering - Introductory Machine Learning

In 2022 I started the new  Machine Learning Specialization by Andrew NG.

I completed it's first two courses in 2022. I'll share the jupyter notebooks from this specialization soon.

Now, in 2023 when I am finally free from my M1, I am going to complete the specialization. Here is the fourth Assignment of course 3 (last course) on Recommender Systems.

 

Procedure:

In Content based filtering, for a movie streaming website, we have a set of users and their ratings to the movies they watched. We can also create engineered features e.g, the average rating that a user gave to each genre.

We then have another dataset that contains all of the movies and their corresponding average ratings and their corresponding one-hot-encoded genres.

The users data is fed into a neural network. The movies data is fed into another neural network. Both neural networks can have different architects, but the final layer of each neural network must be identical to the other one.

The final layers give user and movie/item vectors. The dot product of these two vectors is our prediction.

Mean squared error cost function is used to simultaneously train both neural networks and to reduce the cost.

After training whenever we have a new user and he/she has watched some movies, we can use this information to create a user vector. We can take the product of this user vector with a fix number retrieved movie vectors and rank the movies according to their  prediction magnitudes.

By calculating the distances between movie vectors we can also find which movies are most similar to each other.


Relevant  github link (my github repo)

Relevant  video explanation (by me in Urdu-langauge)

Comments

Post a Comment

Popular posts from this blog

Ai SDK ui - 1 - Learning AI SDK UI with Next.js

I recently started learning AI SDK UI . I’ve been working with Next.js already, but this library adds something special. It's  framework agnostic !  It works with Angular, Vue, Svelte, and React. Of course, I’m doing it with Next.js (React) because that’s what I know best. One of the first things I tried was the useChat hook. This makes building a chatbot UI feel way simpler. Normally, handling messages, updating state, and keeping track of what the AI is saying can be messy. With useChat , a lot of that heavy lifting is done for you. Here are some cool things I noticed about it: Message streaming out-of-the-box. You don't need to implement manual streaming! The hook manages states like input, messages, status, and errors. You don’t need to write all that boilerplate yourself. I also liked that it handles more than just happy paths. For example: You can stop a response midway if you want. It provides it out of the box! There are even helpers for modifying message...
Read more

NMLS - 1. Clustering - Kmeans - Unsupervised Learning - Introductory Machine Learning

In 2022 I started the new  Machine Learning Specialization by Andrew NG. I completed it's first two courses in 2022. I'll sgare the jupyter notebooks from this specialization soon. Now, in 2023 when I am finally free from my M1, I am going to complete the specialization. Here is the first Assignment of course 3 (last course) on Clustering. Procedure : Distances between each data-point and all of the cluster centroids were computed The cluster centroid closest to the each data-point was assigned to that data-point The average positions of all of the data-points in a cluster were computed Corresponding centroids were moved to these new average positions The procedure was repeated for a specific number of iterations. Relevant  github link Relevant  video explanation (in Urdu)
Read more

NMLS - 3. Recommender Systems - Collaborative Filtering - Introductory Machine Learning

  In 2022 I started the new  Machine Learning Specialization by Andrew NG. I completed it's first two courses in 2022. I'll share the jupyter notebooks from this specialization soon. Now, in 2023 when I am finally free from my M1, I am going to complete the specialization. Here is the third Assignment of course 3 (last course) on Recommender Systems.   We were given a dataset which contained 'nu' users and 'nm' movies. The ratings that each user gave to various movies were present in the dataset. The task was: We have a new user who has not yet rated a lot of movies. We have have to recommended him movies based on his previous ratings.   Procedure : The cost function was implemented using For Loop & then without any loop (Vectorized Implementation) A list of movie ratings for a new user was created and was added to the training data The rows(movies) were normalized by taking average of each row for non-zero values and then subtracting each value from the...
Read more