Alternating least squares for personalized ranking

Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user–item preference relation-ship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the sys-tem has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional pre-diction mean squared error. The naive minimization of a ranking objective function is typically expensive. This diffi-culty is usually overcome by a trade-off: sacrificing the accu-racy to some extent for computational efficiency by sampling the objective function. In this paper, we present a compu-tationally effective approach for the direct minimization of a ranking objective function, without sampling. We demon-strate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feed-back recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics