Recommendation for users the new items in the dataset

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Hello ,
i've trained a lightFM model on my users/vehicles datasets , but the thing is that everytime i have new items/users that are added into the dataset , and each vehicle has a salingstate variable that indicates wheither it's still available on the purchase list or not , so the thing is that i want to , each time , try to recommend to the users only the new vehicles that are added in the dataset and are available in the purchase list based on the preferences they have in the interaction matrix. I've tried to build a new item_feature_matrix for the new vehicles and put it in the predict parameters but it doesn't work correctly , so i was wondering if there was any solution to this problem as i saw a lot of people asking for it but no one has been answered.

No source files or tests are named. Start with the LightFM prediction entry point and the interaction and item-feature inputs described in the issue; done means establishing and documenting a reproducible approach for recommending newly added, available vehicles to existing users.

Written by the indexing model from the issue text.