Graph Collaborative Signals Denoising and Augmentation for Recommendation

Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated. In this work, we propose a new graph adjacency matrix that incorporates user-user and item-item correlations, as well as a properly designed user-item interaction matrix that balances the number of interactions across all users. To achieve this, we pre-train a graph-based recommendation method to obtain users/items embeddings, and then enhance the user-item interaction matrix via top-K sampling. We also augment the symmetric user-user and item-item correlation components to the adjacency matrix. Our experiments demonstrate that the enhanced user-item interaction matrix with improved neighbors and lower density leads to significant benefits in graph-based recommendation. Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions. The code is in \url{https://github.com/zfan20/GraphDA}.Comment: Short Paper Accepted by SIGIR 2023, 6 page

ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items). Due to the sample selection bias, the long-tail items lack sufficient records to learn good feature representations, i.e. data sparsity and cold start problems. The resultant distribution discrepancy between displayed and non-displayed items would cause poor long-tail performance. To this end, we propose an entire space adaptation model (ESAM) to address this problem from the perspective of domain adaptation (DA). ESAM regards displayed and non-displayed items as source and target domains respectively. Specifically, we design the attribute correlation alignment that considers the correlation between high-level attributes of the item to achieve distribution alignment. Furthermore, we introduce two effective regularization strategies, i.e. \textit{center-wise clustering} and \textit{self-training} to improve DA process. Without requiring any auxiliary information and auxiliary domains, ESAM transfers the knowledge from displayed items to non-displayed items for alleviating the distribution inconsistency. Experiments on two public datasets and a large-scale industrial dataset collected from Taobao demonstrate that ESAM achieves state-of-the-art performance, especially in the long-tail space. Besides, we deploy ESAM to the Taobao search engine, leading to significant improvement on online performance. The code is available at \url{https://github.com/A-bone1/ESAM.git}Comment: Accept by SIGIR-202

Fairly Adaptive Negative Sampling for Recommendations

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model's unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method's superiority in group fairness enhancement and fairness-utility tradeoff.Comment: Accepted by TheWebConf202

Revisiting Adversarially Learned Injection Attacks Against Recommender Systems

Recommender systems play an important role in modern information and e-commerce applications. While increasing research is dedicated to improving the relevance and diversity of the recommendations, the potential risks of state-of-the-art recommendation models are under-explored, that is, these models could be subject to attacks from malicious third parties, through injecting fake user interactions to achieve their purposes. This paper revisits the adversarially-learned injection attack problem, where the injected fake user `behaviors' are learned locally by the attackers with their own model -- one that is potentially different from the model under attack, but shares similar properties to allow attack transfer. We found that most existing works in literature suffer from two major limitations: (1) they do not solve the optimization problem precisely, making the attack less harmful than it could be, (2) they assume perfect knowledge for the attack, causing the lack of understanding for realistic attack capabilities. We demonstrate that the exact solution for generating fake users as an optimization problem could lead to a much larger impact. Our experiments on a real-world dataset reveal important properties of the attack, including attack transferability and its limitations. These findings can inspire useful defensive methods against this possible existing attack.Comment: Accepted at Recsys 2

JoVA-hinge: joint variational autoencoders for personalized recommendation with implicit feedback

Recently, Variational Autoencoders (VAEs) have shown remarkable performance in collaborative filtering (CF) with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. However, existing VAE-based recommendation models learn user and item representations separately. This thesis introduces joint variational autoencoders (JoVA). JoVA, as an ensemble of two VAEs, simultaneously and jointly learns both user-user and item-item correlations and collectively reconstructs and predicts user preferences. Moreover, a variant of JoVA, referred to as JoVA-Hinge, is introduced to improve recommendation quality. JoVA-Hinge incorporates pairwise ranking loss to VAE's losses. Extensive experiments on multiple real-world datasets show that our model can outperform state-of-the-art under a variety of commonly-used metrics. Our empirical experiments also confirm that JoVA-Hinge offers better results than existing methods for cold-start users with limited training data

Visual Co-occurence Learning using Denoising Autoencoders

Modern recommendation systems are leveraging the recent advances in deep neural networks to provide better recommendations. In addition to making accurate recommendations to users, we are interested in the recommendation of items that are complementary to a set of other items. More specifically, given a user query containing items from different categories, we seek to recommend one or more items from our inventory based on latent representations of their visual appearance. For this purpose, a denoising autoencoder (DAE) is used. The capacity of DAEs to remove the noise from corrupted inputs by predicting their corresponding uncorrupted counterparts is investigated. Used with the right corruption process, we show that they can be used as regular prediction models. Furthermore, we measure experimentally two of their specificities. The first is their capacity to predict any potentially missing variable from their inputs. The second is their ability to predict multiple missing variables at the same time given a limited amount of information at their disposal. Finally, we experiment with the use of DAEs to recommend fashion items that are jointly fashionable with a user query. Latent representations of items contained in the user query are being fed into a DAE to predict the latent representation of the ideal item to recommend. This ideal item is then matched to a real item from our inventory that we end up recommending to the user

Identification of Factors Contributing to Traffic Crashes by Analysis of Text Narratives

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature of text data within narratives has challenged exploration of crash narratives. In response, this dissertation aims to develop an analysis framework and methods to enable the extraction of insights from crash narratives and thus improve our level of understanding of traffic crashes to a new level. The methodological development of this dissertation is split into three objectives. The first objective is to devise an approach for extraction of severity contributing insights from crash narratives by investigating interpretable machine learning and text mining techniques. The second objective is to enable an enhanced identification of crash severity contributors in the form of meaningful phrases by integrating recent advancements in Natural Language Processing (NLP). The third objective is to develop an approach for semantic search of information of interest in crash narratives. The obtained results indicate that the developed approaches enable the extraction of valuable insights from crash narratives to 1) uncover factors that quantitative may not reveal, 2) confirm results from classic statistical analysis on crash data, and 3) fix inconsistencies in quantitative data. The outcomes of this dissertation add substantial value to traffic safety, as the developed approaches allow analysts to exploit the rich information in crash narratives for a more comprehensive and accurate diagnosis of traffic crashes

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others