Before revision: We present SimDex, a new technique for serving exact top-K recommendations on matrix factorization models that measures and optimizes for the similarity between users in the model.
After revision: We present SimDex, a new technique for serving exact top-K predictions for state-of-the-art Matrix Factorization models requires solving the Maximum Inner Product Search problem. Solving MIPS can be computationally expensive, thus spurring the recent development of several indexing techniques for this task. These techniques generally exploit similarity between user or item weights in the similarity between users in the model.