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/Non veg), cuisine(Italian/Chinese.. etc.)]. With respect to 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 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 above parameters and generate recommendation.
Any help would be appreciated, Thanks.