In many commercial systems, the 'best bet' recommendations are shown, but the predicted rating values are not. This is usually referred to as a top-N recommendation task, where the goal of the recommender system is to find a few specific items which are supposed to be most appealing to the user. Common methodologies based on error metrics (such as RMSE) are not a natural fit for evaluating the top-N recommendation task. Rather, top-N performance can be directly measured by alternative methodologies based on accuracy metrics (such as precision/recall). An extensive evaluation of several state-of-the art recommender algorithms suggests that algorithms optimized for minimizing RMSE do not necessarily perform as expected in terms of top-N recommendation task. Results show that improvements in RMSE often do not translate into accuracy improvements. In particular, a naive non-personalized algorithm can outperform some common recommendation approaches and almost match the accuracy of sophisticated algorithms. Another finding is that the very few top popular items can skew the top-N performance. The analysis points out that when evaluating a recommender algorithm on the top-N recommendation task, the test set should be chosen carefully in order to not bias accuracy metrics towards non-personalized solutions. Finally, we offer practitioners new variants of two collaborative filtering algorithms that, regardless of their RMSE, significantly outperform other recommender algorithms in pursuing the top-N recommendation task, with offering additional practical advantages. This comes at surprise given the simplicity of these two methods

Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs

Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process.In this work, we review existing works that consider information from such sequentially ordered user-item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we callsequence-aware recommender systems, and outline open challenges in the area.

Most recommender systems work on single domains, i.e., they recommend items related to the same domain where users have expressed ratings. However, the integration of different domains into one recommender system could allow users to span over different types of items. For instance, users that have watched live TV programs could like to be recommended with on-demand movies, music, mobile applications, friends to connect to, etc. This paper focuses on cross-domain collaborative recommender systems, whose aim is to suggest items related to multiple domains. We first formalize the cross-domain problem in order to provide a common framework for the classification and the evaluation of state-of-the-art algorithms. We later define a new class of cross-domain algorithms based on neighborhood collaborative filtering, either item-based or user-based. The main idea is to first model the classical similarity relationships (e.g., Pearson, cosine) as a direct graph and to later explore all possible paths connecting users or items in order to find new, cross-domain, relationships. The algorithms have been tested on three cross-domain scenarios artificially reproduced by partitioning the Netflix dataset.