I am trying to develop a basic book recommender system to get in touch with the field and start learning methods and how to prepare the data.
The Dataframe I am using is pretty plain, it has the following structure (this is a simplified example):
number type username product publishing_dt price genres 0 34 access kerrigan 130365 2019-12-10 16.99 fantasy, kids 1 1 order kerrigan 76863 2020-01-15 4.66 action, crime 2 1 order 45michael 76863 2020-01-15 4.66 action, crime 3 1 order kerrigan 76863 2020-01-15 4.66 action, crime 4 1 order 45michael 86833 2020-02-4 15.65 adventure 5 1 order 45michael 86833 2020-02-4 15.65 adventure 6 1 order 45michael 130365 2019-12-10 16.99 fantasy, kids 7 1 order alicia7 130365 2019-12-10 16.99 fantasy, kids 8 1 order alicia7 130365 2019-12-10 16.99 fantasy, kids 9 1 order john5 86833 2020-02-4 15.65 adventure 10 1 order john5 86833 2020-02-4 15.65 adventure 11 10 access 45michael 63767 2020-01-24 12.99 adventure, fantasy 12 1 order uololo 830166 2019-11-03 18.45 action, war 13 25 access 7762hc 84325 2019-11-04 25.60 romance 14 4 access adrian12 997165 2019-12-16 9.99 health, motivational 15 1 order kylemm 537077 2019-12-25 8.55 history 16 31 access yvera 76863 2020-01-15 4.66 action, crime 17 1 order kerrigan 1023897 2020-02-03 7.99 adventure, humor 18 1 order angel8 86217 2020-02-01 14.99 guide, travel
To make item-based recommendations (which only take into account metadata information) I've used Count Vectorizer and Cosine Similarity.
For purchase-history-based recommendations, for now, I'm also using Cosine Similarity, I am calculating a similarity score between users, the run order would be this one:
- Calculate the most similar users to the one we want to get recommendations for.
- From those users, get their purchase history.
- Delete from that list those books the user has already bought.
- Return the final list with the recommendations.
In this case, I've tried to apply KNN but the truth is I just couldn't and finally discarded it, but I think that with this data Cosine Similarity is a good choice.
The last scenario I would like to contemplate is the classic "According to items you've seen..." (I don't know how to properly translate that into English, but you get the idea). That's why the number field in the Dataframe is there, it shows the number of times certain user accessed the book file but didn't purchase it. So I've been thinking of various options:
- From all the books the user has accessed, take, for example, a random one and return new recommendations based on that one. This could end up being a pretty bad choice as people can make clicks on certain items without even being interested in them.
- Search for similarities between the books the user has been seeing and search for new books with those characteristics. This seems like the correct choice, but I don't really know how to take it into action, it looks like a KNN approach.
- Simply show the user those items he has been seeing, ordered by number of accesses (the more he accessed the file, the more he is interested in it).
Everything in Python, of course, I'm here searching for some help on where to start on this last scenario specifically, and if someone can recommend me a good tutorial or guide on how to apply KNN in similar cases to this, where I don't have any kind of ratings (feature that I've been noticing it has a lot of weight on recommender systems).