How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

News Session-Based Recommendations using Deep Neural Networks

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, fast growing number of items, accelerated item's value decay, and users preferences dynamic shift. Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent Neural Networks. In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems. This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks. The recommendation task addressed in this work is next-item prediction for users sessions: "what is the next most likely article a user might read in a session?" Users sessions context is leveraged by the architecture to provide additional information in such extreme cold-start scenario of news recommendation. Users' behavior and item features are both merged in an hybrid recommendation approach. A temporal offline evaluation method is also proposed as a complementary contribution, for a more realistic evaluation of such task, considering dynamic factors that affect global readership interests like popularity, recency, and seasonality. Experiments with an extensive number of session-based recommendation methods were performed and the proposed instantiation of CHAMELEON meta-architecture obtained a significant relative improvement in top-n accuracy and ranking metrics (10% on Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada. https://recsys.acm.org/recsys18/dlrs

An ensemble approach of recurrent neural networks using pre-trained embeddings for playlist completion

This paper describes the approach of the D2KLab team to the RecSys Challenge 2018 that focuses on the task of playlist completion. We propose an ensemble strategy of different recurrent neural networks leveraging pre-trained embeddings representing tracks, artists, albums, and titles as inputs. We also use lyrics from which we extract semantic and stylistic features that we fed into the network for the creative track. The RNN learns a probabilistic model from the sequences of items in the playlist, which is then used to predict the most likely tracks to be added to the playlist. Concerning the playlists without tracks, we implemented a fall-back strategy called Title2Rec that generates recommendations using only the playlist title. We optimized the RNN, Title2Rec, and the ensemble approach on a validation set, tuning hyper-parameters such as the optimizer algorithm, the learning rate, and the generation strategy. This approach is effective in predicting tracks for a playlist and flexible to include diverse types of inputs, but it is also computationally demanding in the training phase

Towards the Evaluation of Recommender Systems with Impressions

In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study's goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain

Impressions in Recommender Systems: Present and Future

Impressions are a novel data source providing researchers and practitioners with more details about user interactions and their context. In particular, an impression contain the items shown on screen to users, alongside users' interactions toward such items. In recent years, interest in impressions has thrived, and more papers use impressions in recommender systems. Despite this, the literature does not contain a comprehensive review of the current topics and future directions. This work summarizes impressions in recommender systems under three perspectives: recommendation models, datasets with impressions, and evaluation methodologies. Then, we propose several future directions with an emphasis on novel approaches. This work is part of an ongoing review of impressions in recommender systems

Workshop on Learning and Evaluating Recommendations with Impressions (LERI)

Recommender systems typically rely on past user interactions as the primary source of information for making predictions. However, although highly informative, past user interactions are strongly biased. Impressions, on the other hand, are a new source of information that indicate the items displayed on screen when the user interacted (or not) with them, and have the potential to impact the field of recommender systems in several ways. Early research on impressions was constrained by the limited availability of public datasets, but this is rapidly changing and, as a consequence, interest in impressions has increased. Impressions present new research questions and opportunities, but also bring new challenges. Several works propose to use impressions as part of recommender models in various ways and discuss their information content. Others explore their potential in off-policy-estimation and reinforcement learning. Overall, the interest of the community is growing, but efforts in this direction remain disconnected. Therefore, we believe that a workshop would be useful in bringing the community together