I am developing a website, which will recommend recipes to the visitors based on their data. I am collecting data from their profile, website activity, and Facebook.
Currently, I have data like [username/userId, rating of recipes, age, gender, type(veg/Nonveg), cuisine(Italian/Chinese.. etc.)]. With respect to the above features, I want to recommend new recipes which they have not visited.
I have implemented ALS (alternating least squares) spark algorithm. In this we have to prepare csv which contains [userId,RecipesId,Rating] columns. Then we have to train this data and create the model by adjusting parameters like lamdas, Rank, iteration. This model generated a recommendation, using pyspark
The ALS algorithm accepts only three features userId, RecipesId, Rating. I am unable to include more features (like type, cuisine, gender, etc.) apart from which I have mentioned above (userId, RecipesId, Rating). I want to include those features, then train the model and generate recommendations.
Is there any other algorithm in which I can include the above parameters and generate a recommendation.
Any help would be appreciated, Thanks.