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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

model.recommendProducts(userId, numberOfRecommendations)

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.

1 Answer

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by (38.5k points)

Firstly, I would like to suggest you the most widely used algorithm that is “Logistic Regression”. In this algorithm, the probabilities describing the possible outcomes of a single trial is modelled using a logistic function.One of the advantages of using logistic regression is, it is very useful in understanding the influence of several independent variables on a single outcome variable.

You can use other algorithms also like:

1.Naive Bayes classifier

2.Support Vector Mechanism(SVM)

3.Collaborative filtering

If you wish to know more about the Support Vector Machine visit this Support Vector Machine Tutorial.

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