# recommender systems : how to deal with items that change over time?

Let's say I am building a recommender system where items change through time. We suppose that each transaction is composed of :

• an item $$i$$ in list of items $$(i_1, i_2, i_3, .., i_m)$$.
• a user $$u$$ in list of users $$(u_1, u_2, u_3, ..., u_n)$$.
• a date $$t$$ in list of dates $$(t_1, t_2, ... t_k)$$.

We suppose that items have underlying features that change over time.For example, If we consider retail products, underlying features could be :

• The discount level that is applied on the item when the customer has purchased the transaction (5%, 10%, 20%, 30%, ...).

An other example, if we consider financial stocks, underlying features that change over time could be :

• The stock situation at time of the transaction (underpriced or overpriced).
• The stock's central bank politics at time of the transaction (low interest rates, medium interest rates, high interest rates).

We suppose that these underlying features have a strong impact on users. It completly drives their decisions to buy or not an object. If we consider two items $$i_1$$ and $$i_2$$, at time $$t_1$$, a given user $$u_1$$ could prefer $$i_1$$ over $$i_2$$ because $$i_1$$'s underlying features are more interesting than $$i_2$$'s. If we consider a different time, maybe $$u_1$$ could be more interested in $$i_2$$ than $$i_1$$.

My question is : how to take into account underlying features that change over time in recommender systems such as user-user collaborative filtering, SVD, ALS... ?.