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

Characterizing Impression-Aware Recommender Systems

Impression-aware recommender systems (IARS) are a type of recommenders that learn user preferences using their interactions and the recommendations (also known as impressions) shown to users. The community’s interest in this type of recommenders has steadily increased in recent years. To aid in characterizing this type of recommenders, we propose a theoretical framework to define IARS and classify the recommenders present in the state-of-the-art. We start this work by defining core concepts related to this type of recommenders, such as impressions and user feedback. Based on this theoretical framework, we identify and define three properties and three taxonomies that characterize IARS. Lastly, we undergo a systematic literature review where we discover and select papers belonging to the state-of-the-art. Our review analyzes papers under the properties and taxonomies we propose; we highlight the most and least common properties and taxonomies used in the literature, their relations, and their evolution over time, among others

Lightweight Model for Session-Based Recommender Systems with Seasonality Information in the Fashion Domain

This paper presents the solution designed by the team "Boston Team Party"for the ACM RecSys Challenge 2022. The competition was organized by Dressipi and was framed under the session-based fashion recommendations domain. Particularly, the task was to predict the purchased item at the end of each anonymous session. Our proposed two-stage solution is effective, lightweight, and scalable. First, it leverages the expertise of several strong recommendation models to produce a pool of candidate items. Then, a Gradient-Boosting Decision Tree model aggregates these candidates alongside several hand-crafted features to produce the final ranking. Our model achieved a score of 0.18800 in the public leaderboard. To aid in the reproducibility of our findings, we open-source our materials

Measuring the ranking quality of recommendations in a two-dimensional carousel setting

Movie-on-demand and music streaming services usually provide the user with multiple recommendation lists, i.e., carousels, in a two-dimensional user interface, each generated according to different criteria (e.g., TV series, popular artists, etc.). In this two-dimensional setting it is not appropriate to use traditional ranking metrics designed for a single ranking list. It is well known that users do not explore a two-dimensional interface one row at a time, but rather focus their attention in a triangular area at the top-left corner. Furthermore, it is frequent for user interfaces to hide some items or lists due to space constraints, which can be shown by performing certain actions (i.e., click, swipe). In this paper we extend the widely used NDCG to a two-dimensional recommendation setting with a formulation that allows to account both the two-dimensional user exploration behaviour and interface-specific design. We also compare the proposed extension against single-list NDCG highlighting that they can lead to a different choice of the optimal algorithm in offline evaluation

ContentWise Impressions: An Industrial Dataset with Impressions Included

In this article, we introduce the dataset dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet. The dataset is distinguished from other already available multimedia recommendation datasets by the availability of impressions, idest the recommendations shown to the user, its size, and by being open-source. We describe the data collection process, the preprocessing applied, its characteristics, and statistics when compared to other commonly used datasets. We also highlight several possible use cases and research questions that can benefit from the availability of user impressions in an open-source dataset. Furthermore, we release software tools to load and split the data, as well as examples of how to use both user interactions and impressions in several common recommendation algorithms

Search for new particles in events with one lepton and missing transverse momentum in pp collisions at root s=8 TeV with the ATLAS detector

This paper presents a search for new particles in events with one lepton (electron or muon) and missing transverse momentum using 20.3 fb(-1) of proton-proton collision data at root s = 8 TeV recorded by the ATLAS experiment at the Large Hadron Collider. No significant excess beyond Standard Model expectations is observed. A W' with Sequential Standard Model couplings is excluded at the 95% confidence level for masses up to 3.24 TeV. Excited chiral bosons (W*) with equivalent coupling strengths are excluded for masses up to 3.21 TeV. In the framework of an effective field theory limits are also set on the dark matter-nucleon scattering cross-section as well as the mass scale M-* of the unknown mediating interaction for dark matter pair production in association with a leptonically decaying W