Abstract: Particle Thompson Sampling for online matrix factorization recommendation is described. In one or more implementations, a recommendation system provides a recommendation of an item to a user using Thompson Sampling. The recommendation system then receives a rating of the item from the user. Unlike conventional solutions which only update the user latent features, the recommendation system updates both user latent features and item latent features in a matrix factorization model based on the rating of the item. The updating is performed in real time which enables the recommendation system to quickly adapt to the user ratings to provide new recommendations. In one or more implementations, to update the user latent features and the item latent features in the matrix factorization model, the recommendation system utilizes a Rao-Blackwellized particle filter for online matrix factorization.

Abstract: Campaign effectiveness determination techniques and systems are described that are usable to determine campaign effectiveness with improved accuracy and computing performance by reduction of confounding bias through dimension reduction. In one example, campaign data that pertains to first and second campaign groups is characterized using a plurality of features that describe subjects included in the first and second campaign groups. The characterized campaign data is projected, automatically and without user intervention, for the first and second campaign groups into a reduced dimension space, e.g., using linear or non-linear techniques. Subjects in the first and second campaign groups are associated, one to another using the projected campaign data, such that a number of subjects in the first campaign group is matched against a number of subjects in the second campaign group.

Abstract: Particle Thompson Sampling for online matrix factorization recommendation is described. In one or more implementations, a recommendation system provides a recommendation of an item to a user using Thompson Sampling. The recommendation system then receives a rating of the item from the user. Unlike conventional solutions which only update the user latent features, the recommendation system updates both user latent features and item latent features in a matrix factorization model based on the rating of the item. The updating is performed in real time which enables the recommendation system to quickly adapt to the user ratings to provide new recommendations. In one or more implementations, to update the user latent features and the item latent features in the matrix factorization model, the recommendation system utilizes a Rao-Blackwellized particle filter for online matrix factorization.

Abstract: A temporal prediction model is described that is usable to predict user purchase behavior for an online advertising instance. The temporal prediction model may be formed by processing time windows for click data, conversion data, and side information. In one or more implementations, temporal dynamics are applied to the click data, the conversion data, and/or the side information via the processed time windows. Various processing techniques of the temporal prediction model may utilize the applied temporal dynamics to predict user purchase behavior and/or effectiveness of an online advertising instance.