Using RTD for recommendations from large number of items
By Michel Adar on Dec 07, 2009
Traditional approaches to these situations include Market Basket Analysis and Collaborative Filtering. Collaborative filtering has its strength in extracting affinity information from ratings, and a good CF algorithm can exploit ratings data to extract the least bit of information from it. So these traditional approaches do have their advantages, but nevertheless, they are clearly limited in the following ways:
- They can not recommend new items
- They can not issue recommendations to new users
- They require vast numbers of baskets or ratings to cover the space with statistically significant data
- They do not provide flexibility in selecting recommendations to optimize for varying and conflicting business goals
With RTD we are capable of overcoming these limitations by using a technique that does not necessarily involve clustering of items or users, and does not start from scratch for every new item.
Intuitively, it should be clear that the recommendation of the movie "Terminator 3" will follow similar patterns to "Terminator 2", so when T3 appears, the knowledge about T2 can be used as a good approximation. Similarly, the demographic and behavioral data about a user together with the context of an interaction can give us big clues of what the person will be interested n, even if we have not seen any purchase or ratings from that person.
The way we do item recommendations with RTD in this context is to compute the likelihood that an item will be of interest by dividing the likelihood computation into a two layer model network where the base layer computes the affinity of the user with the characteristics of the item and the second layer uses one model to blend the results of the first layer into one final prediction.