Tutorial 5- Content Based Recommendation System

In this video, we will learn about the Content based Recommender Systems. This type of recommender system is dependent on the inputs provided by the user. Some of the typical examples of content based filtering are Google, Wikipedia etc. Let’s start by getting an intuition of what these recommenders do. When you search a group of keywords, the most basic idea will be to show all the items containing those keywords
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[@anujsaboo7081]

The diagram for collaborative filtering is what seems to match with your explanation of Content Based Systems at the starting of the video. Is the explanation correct?

[@sidexplores]

This is such great stuff! Happy I came across your channel

[@anujsaboo7081]

In the above video, you have used Sigmoid Function to squish values into [0,1]. We know that Pearson would range the values from [-1,1]. So can we use any, or is there some logic behind choosing one of the above?

[@aleksahadzic9302]

Thanks for this. My dataset has tv shows and i have decided to use it for my recommendation algorhitm. I am planning to convert the tv show description to vectors and then recommend based on the distance to other shows

[@kacharlabhargav21]

Your explanation at the start is about collaborative filtering

[@rohanebenezer9896]

Yes...i too felt that

[@virendrapatil73]

Hello Krish sir,
the explanation you gave for content based filtering is actually working of collaborative filtering.....I read it on google... Is it the case?

[@mohamedaminelazreg3157]

Actually yes, he explained the CF approach , I dunno why he said that it was CB

[@etietopudofia818]

Thank you so much for your explanation..

[@SatyamShivam_01]

which algorithm is actually being used in this content based filtering 🤔???

[@greeshmababu4751]

All your videos are very clear and easy to understand, Thank you! waiting eagerly for your NLP playlist!!!!

[@krishnaik06]

Check in my channel

[@anayraikwarvlogs]

Hi krish, nice and clear video but only one concern. How you are doing similarity of vectors that were based out of count vectorizer system which doesn't include semantic meaning?? Does this recommendation worth showing output with relevancy??

[@hardishah3188]

how can i make a fastapi of this model? I have also watched ur fastapi wala tutorial, but still have errors

[@YT-yt-yt-3]

With all due respect, you ended up explaining collaborative filtering while you intended to explain content based filtering. Content based filtering works without other user interaction. Perhaps you could add a note in the video.

[@channadissanayaka6450]

Thank you sir

[@hekkta]

Great video, I learned a lot. Thank you.

[@syedfaizan4416]

Outstandingly Explained!

[@sorvex9]

How do I make a content-based recommender with online learning? The problem with this method is, that you have to reconstruct a TFIDF matrix every time a new word needs to get added to vocabulary.

[@SumanKumari-cn3wo]

Hello sir, in which line the ml modal code is written for this recommendation system?

[@abhayvashokan5580]

In ln [46] of the jupyter notebook (displaying sig[0]), the first value should be 1 right (relation between the description of movie 1 and movie 1)?

[@karthikeyanviswanathan4725]

amazing job brother ! keep posting these kind of videos

[@sid9426]

Hi Krish,
I just can't seem to find your NLP playlist. Could you please help me with that?

[@nehamanpreet1044]

Sir please make videos on MATRIX FACTORISATION, NMF, SVD AND LDA FOR TOPIC MODELLING

[@IT_FoodLover]

Good job sir

[@priyankharshadbardolia3978]

In my case the Sparse matrix formed after Tfidf is too large and when I compute cosine similarity on it, its giving me memory error. Any idea on how to solve it?

[@bedramtamang1439]

Amazing

[@louerleseigneur4532]

Thanks Krish

[@xianz2609]

Hi, if this method is applied on a larger dataset, "nnz of the result is too large" will occur. Any workaround for this? Thanks

[@zeeshanchitapure5519]

I am also facing same problem. if any solution please share. thank you

[@suvarnadhiraj]

The video is very rushed, without clear explanation of why description was chosen for recommendation

[@josefftan372]

thank you sir!

[@sagarsrivastav7700]

Sir, Can we train our model on genres instead of overview?

[@rajatkesarwani4552]

Sir, your NLP playlist is not available to the public. Can, u please make it public. So, that we can learn the NLP concepts.

[@krishnaik06]

Hi @Rajat I am rewamping by NLP playlist...there also some mistakes which I want to rectify..so it will be uploaded soon

[@lijinakv7006]

can you please explain matrix factorization(SVD)?

[@piyushpawar75]

I got an error list object is not callable

[@paknbagn9917]

sigmoid_kernel is really heavy process , is there any better way ?

[@abhaychinchole3482]

can you upload content based recommendation system for e-commerce website like amazon?

[@pushpendudas3372]

May I get the code.

[@shivanireddy4701]

github.com/krishnaik06/Recommendation_complete_tutorial/blob/master/Content%20Based%20Recommendation%20Engines%20using%20Python.ipynb

[@ibrahimhussain6200]

How can I check it for a new data point??

[@HimanshuGupta-hc5qt]

How could i get the code for this project

[@techandroid2845]

sir please share source code

[@General_studies_by_ARD]

please , provide code link

[@rohanebenezer9896]

Gui please

[@khanuhabib]

Does anyone know which loss function we use for recommender system?

[@shauryasingh8576]

Square loss function