title: In machine learning which algorithm should I use to recommend, based on different features like rating,type,gender etc
body: <p>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.</p>
  
  <p>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.</p>
  
  <p>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 </p>
  
  <blockquote>
    <p>model.recommendProducts(userId, numberOfRecommendations)</p>
  </blockquote>
  
  <p>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. </p>
  
  <p>Is there any other algorithm in which I can include above parameters and generate recommendation. </p>
  
  <p>Any help would be appreciated, Thanks.</p>