Alternating Least Squares with Incremental Learning Bias

Abstract

Recommender systems provide personalized suggestions for every individual user in the system. Many recommender systems use collaborative filtering approach in which the system collects and analyzes users' past behaviors, activities or preferences to produce high quality recommendations for the users. Among various collaborative recommendation techniques, model-based approaches are more scalable than memory-based approaches for large scale data sets in spite of large offline computation and difficulty to update the model in real time. In this paper, we introduce Alternating Least Squares with Incremental Learning Bias (ALS++) algorithm to improve over existing matrix factorization algorithms. These learning biases are treated as additional dimensions in our algorithm rather than as additional weights. As the learning process begins after regularized matrix factorization, the algorithm can update incrementally over the preference changes of the data set in constant time without rebuilding the new model again. We set up two different experiments using three different data sets to measure the performance of our new algorithm.