Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system. We find that both the relation type and the relation value are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference. The first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item in recommending the target item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with the recommendation task of preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by the modeling of multiple item relations

Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world scenarios, e.g., two movies share the same director, two products complement with each other, etc. Distinct from the collaborative similarity that implies co-interact patterns from the user's perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF) to exploit multiple item relations in recommender systems. We find that both the relation type (e.g., shared director) and the relation value (e.g., Steven Spielberg) are crucial in inferring user preference. To this end, we develop a two-level hierarchical attention mechanism to model user preference - the first-level attention discriminates which types of relations are more important, and the second-level attention considers the specific relation values to estimate the contribution of a historical item. To make the item embeddings be reflective of the relational structure between items, we further formulate a task to preserve the item relations, and jointly train it with user preference modeling. Empirical results on two real datasets demonstrate the strong performance of RCF1. Furthermore, we also conduct qualitative analyses to show the benefits of explanations brought by RCF's modeling of multiple item relations

Toward Responsible Recommender Systems

Recommender systems have become essential conduits: they can shape the media we consume, the jobs we seek, and even the friendships and professional contacts that form our social circles. With such a wide usage and impact, recommender systems can exert strong, but often unforeseen, and sometimes even detrimental influence on the social processes connected to culture, lifestyles, politics, education, ethics, economic well-being, and even social justice. Hence, in this dissertation research, we aim to identify, analyze, and alleviate potential risks and harms on users, item providers, the platforms, and ultimately the society, and to lay the foundation for new responsible recommender systems. In particular, we make three unique contributions toward responsible recommender systems: • First, we study how to counteract the exposure bias in user-item interaction data. To overcome the challenge that the user-item exposure information is hard to be estimated when aiming to produce unbiased recommendations, we develop a novel combinational joint learning framework to learn unbiased user-item relevance and unbiased user-item exposure information simultaneously. Then, we push the problem to an extreme where we aim to predict relevance for items with zero exposure in the interaction data. For this, we propose a neural network utilizing a randomized training mechanism and a Mixture-of-Experts Transformation structure. Experiments validate the effective performance by the proposed methods. • Second, we study what bias the machine learning based recommendation algorithms can bring and how to alleviate these bias. We uncover the popularity-opportunity bias on items and the mainstream bias on users. We conduct extensive data-driven study to show the existence of these bias in fundamental recommendation algorithms. Then, we explore and propose potential solutions to relieve these two types of bias, which empirically demonstrate outstanding performance for debiasing. • At last, we move our attention to the problem of how to measure and enhance fairness in recommendation results. We study the recommendation fairness in three different recommendation scenarios – the multi-dimension recommendation scenario, the personalized ranking recommendation scenario, and the cold-start recommendation scenario. With respect to different recommendation scenarios, we develop different algorithms to enhance the recommendation fairness. We also conduct extensive experiments to empirically show the effectiveness of the proposed solutions

Local Latent Space Models for Top-N Recommendation Appendix

This report shows the local ranks $f^c$ for which rLSVD and rGLSVD achieve their best performance, in terms of HR and ARHR. It is the appendix of the paper "Local Latent Space Models for Top-N Recommendation"