Before revision: Making 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.
After revision: Making top-K predictions for state-of-the-art Matrix Factorization models requires solving the Maximum Inner Product Search (MIPS) is an important task that is widely pertinent to recommender systems and high-dimensional similarity search. The brute-force approach to solving exact MIPS is computationally expensive, thus spurring the recent development of several indexing techniques for this task.