Statistical biases in Information Retrieval metrics for recommender systems

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposesThis work was partially supported by the national Spanish Government (grants nr. TIN2013-47090-C3-2 and TIN2016-80630-P). We wish to express our gratitude to the anonymous reviewers whose insightful and generous feedback guided us in producing an enhanced version of the paper beyond the amendmentof flaws and shortcoming

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Novel and Diverse Recommendations by Leveraging Linear Models with User and Item Embeddings

[Abstract] Nowadays, item recommendation is an increasing concern for many companies. Users tend to be more reactive than proactive for solving information needs. Recommendation accuracy became the most studied aspect of the quality of the suggestions. However, novel and diverse suggestions also contribute to user satisfaction. Unfortunately, it is common to harm those two aspects when optimizing recommendation accuracy. In this paper, we present EER, a linear model for the top-N recommendation task, which takes advantage of user and item embeddings for improving novelty and diversity without harming accuracy.This work was supported by project RTI2018-093336-B-C22 (MCIU & ERDF), project GPC ED431B 2019/03 (Xunta de Galicia & ERDF) and accreditation ED431G 2019/01 (Xunta de Galicia & ERDF). The first author also acknowledges the support of grant FPU17/03210 (MCIU)Xunta de Galicia; ED431B 2019/03Xunta de Galicia; ED431G 2019/0

Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure

Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse

This paper calls attention to the missing component of the recommender system evaluation process: Statistical Inference. There is active research in several components of the recommender system evaluation process: selecting baselines, standardizing benchmarks, and target item sampling. However, there has not yet been significant work on the role and use of statistical inference for analyzing recommender system evaluation results. In this paper, we argue that the use of statistical inference is a key component of the evaluation process that has not been given sufficient attention. We support this argument with systematic review of recent RecSys papers to understand how statistical inference is currently being used, along with a brief survey of studies that have been done on the use of statistical inference in the information retrieval community. We present several challenges that exist for inference in recommendation experiment which buttresses the need for empirical studies to aid with appropriately selecting and applying statistical inference techniques

Improving accountability in recommender systems research through reproducibility

Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving toward fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human–Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem and facilitate progress in the field by increasing the accountability of researchThis work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference: PID2019-108965GB-I00