Mohamed Reda Bouadjenek

A number of recent works have made seminal contributions to the understanding of user intent and recommender interaction in conversational recommendation. However, to date, these studies have not focused explicitly on context-driven interaction that underlies the typical use of more pervasive Question Answering (QA) focused conversational assistants like Amazon Alexa, Apple Siri, and Google Assistant. In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. In our analysis of this intrinsically organic human-to-human conversation, we observe a clear structure of interaction workflow consisting of a preference elicitation and refinement stage, followed by inquiry and critiquing stages after the first recommendation. To better understand the nature of these stages and the conversational flow within them, we augment existing taxonomies of intent and action to label all interactions at each stage and analyze the workflow. From this analysis, we identify distinct conversational characteristics of each stage, e.g., (i) the preference elicitation stage consists of significant iteration to clarify, refine, and obtain a mutual understanding of preferences, (ii) the inquiry and critiquing stage consists of extensive informational queries to understand features of the recommended item and to (implicitly) specify critiques, and (iii) explanation appears to drive a substantial portion of the post-recommendation interaction, suggesting that beyond the purpose of justification, explanation serves a critical role to direct the evolving conversation itself. Altogether, we contribute a novel qualitative and quantitative analysis of workflow in conversational recommendation that further refines our existing understanding of this important frontier of conversational systems and suggests a number of critical avenues for further research to better automate natural recommendation conversations.

Online recommendation systems are prone to create filter bubbles, whereby users are only recommended content narrowly aligned with their historical interests. In the case of media recommendation, this can reinforce political polarization by recommending topical content (e.g., on the economy) at one extreme end of the political spectrum even though this topic has broad coverage from multiple political viewpoints that would provide a more balanced and informed perspective for the user. Historically, Maximal Marginal Relevance (MMR) has been used to diversify result lists and even mitigate filter bubbles, but suffers from three key drawbacks: (1) MMR directly sacrifices relevance for diversity, (2) MMR typically diversifies across all content and not just targeted dimensions (e.g., political polarization), and (3) MMR is inefficient in practice due to the need to compute pairwise similarities between recommended items. To simultaneously address these limitations, we propose a novel methodology that trains Concept Activation Vectors (CAVs) for targeted topical dimensions (e.g., political polarization). We then modulate the latent embeddings of user preferences in a state-of-the-art VAE-based recommender system to diversify along the targeted dimension while preserving topical relevance across orthogonal dimensions. Our experiments show that our Targeted Diversification VAE-based Collaborative Filtering (TD-VAE-CF) methodology better preserves relevance of content to user preferences across a range of diversification levels in comparison to both untargeted and targeted variations of Maximum Marginal Relevance (MMR); TD-VAE-CF is also much more computationally efficient than the post-hoc re-ranking approach of MMR.

This paper proposes a novel mask-based output layer for multi-level hierarchical classification, addressing the limitations of existing methods which (i) often do not embed the taxonomy structure being used, (ii) use a complex backbone neural network with n disjoint output layers that do not constraint each other, (iii) may output predictions that are often inconsistent with the taxonomy in place, and (iv) have often a fixed value of n. Specifically, we propose a model agnostic output layer that embeds the taxonomy and that can be combined with any model. Our proposed output layer implements a top-down divide-and-conquer strategy through a masking mechanism to enforce that predictions comply with the embedded hierarchy structure. Focusing on image classification, we evaluate the performance of our proposed output layer on three different datasets, each with a three-level hierarchical structure. Experiments on these datasets show that our proposed mask-based output layer allows to improve several multi-level hierarchical classification models using various performance metrics.

Recommendation systems are now an integral part of our daily lives. We rely on them for tasks such as discovering new movies, finding friends on social media, and connecting job seekers with relevant opportunities. Given their vital role, we must ensure these recommendations are free from societal stereotypes. Therefore, evaluating and addressing such biases in recommendation systems is crucial. Previous work evaluating the fairness of recommended items fails to capture certain nuances as they mainly focus on comparing performance metrics for different sensitive groups. In this paper, we introduce a set of comprehensive metrics for quantifying gender bias in recommendations. Specifically, we show the importance of evaluating fairness on a more granular level, which can be achieved using our metrics to capture gender bias using categories of recommended items like genres for movies. Furthermore, we show that employing a category-aware fairness metric as a regularization term along with the main recommendation loss during training can help effectively minimize bias in the models’ output. We experiment on three real-world datasets, using five baseline models alongside two popular fairness-aware models, to show the effectiveness of our metrics in evaluating gender bias. Our metrics help provide an enhanced insight into bias in recommended items compared to previous metrics. Additionally, our results demonstrate how incorporating our regularization term significantly improves the fairness in recommendations for different categories without substantial degradation in overall recommendation performance.