What is the difference between collaborative and content based recommendation system?



I would like to utilize product images and product text description as a source of information for making product recommendations. My question is, will this type of recommendation fall under collaborative or content based approach?


Hi @erigits,

I think it would be content based, as product information is considered as “content”. Whereas in collaborative filtering, user’s behavior is more important as a feature.

Read this resource on recommendation systems



Before looking at the selection process, we must look at the difference b/w collaborative and content based recommendation engine.

Collaborative Recommendation Engine:

Collaborative algorithm uses “User Behaviour” for recommending items. They exploit behaviour of other users and items in terms of transaction history, ratings, selection and purchase information. Other users behaviour and preferences over the items are used to recommend items to the new users. In this case, features of the items are not known. For binary rating (like or not), we can use a similarity measure like Jaccard Similarity to compute item similarity.

Content-Based Recommendation

The point of content-based is that we have to know the content of both user and item. Usually we construct user-profile and item-profile using the content of shared attribute space. For example, for a movie, you represent it with the movie stars in it and the genres (using a binary coding for example). For user profile, you can do the same thing based on the users likes some movie stars/genres etc. To calculate how good a movie is to a user, we use cosine similarity:

Here, you have product attributes like image (Size, dimension, color etc…) and text description about the product then I would say it is more inclined towards “Content Based Recommendation”.

Hope this helps.