Online Prediction of User Actions through an Ensemble Vote from Vector Representation and Frequency Analysis Models
Changsung Moon, Dakota Medd, Paul C. Jones, Steve Harenberg, William Oxbury, Nagiza F. Samatova · 2016
The history of interactions between a user and a piece of technology can be represented as a sequence of actions. The ability to predict a user's next action is useful to many applications. For example, a user-interface that can anticipate the actions of a user is able to provide a more positive experience through just-in-time recommendations and pro-actively allocating or caching resources. Existing sequence prediction techniques have failed to address some of the challenges associated with this task, such as predicting an action that has never appeared for a given context. Techniques for an analogous task in the field of Natural Language Processing (NLP) avoid this issue; however, applying these NLP techniques directly to user action prediction would result in the loss of action frequency and action order, both of which are critically important. Therefore, we propose a method that unifies ideas from NLP with the task of sequence prediction. Our method, Frequency Vector (FVEC) prediction, is an online algorithm that predicts the top-N most likely next actions by combining scores from two models: a frequency analysis model and a vector representation model. In the frequency model, the score of an action is calculated based on the frequency that the action has occurred right after a given context. In the vector representation model, a vector for each action is learned, and a score for an action is calculated based on the similarity of its vector and the mean of the vectors for each action in a given context. Evaluations of FVEC on three real-world datasets resulted in a consistently higher prediction accuracy (and lower standard deviation) than all tested sequence prediction algorithms.