Jiliang Tang

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

2018

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process …

Cloud Information Retrieval: Model Description and Scheme Design

2018 · IEEE Access

The fast development of cloud technology has brought about a new trend in the field of information service: more and more information is being transferred to the cloud as requested. However, the data, such as …

Dynamic Graph Neural Networks

2018 · arXiv (Cornell University)

Graphs, which describe pairwise relations between objects, are essential representations of many real-world data such as social networks. In recent years, graph neural networks, which extend the neural network models to graph data, have attracted …

Signed Graph Convolutional Network

2018 · arXiv (Cornell University)

Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and …

Recent Advances in Multimodal Educational Data Mining in K-12 Education

2020

Recently we have seen a rapid rise in the amount of education data available through the digitization of education. This huge amount of education data usually exhibits in a mixture form of images, videos, speech, …

Node Similarity Preserving Graph Convolutional Networks

2020 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the graph structure and node features by aggregating and transforming information within …

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

2021

Deep learning-based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimension of categorical variables (e.g., user/item identifiers) and meaningfully transform them in the low-dimensional space. The majority of existing DLRSs …

Towards Neural Scaling Laws on Graphs

2024 · arXiv (Cornell University)

Deep graph models (e.g., graph neural networks and graph transformers) have become important techniques for leveraging knowledge across various types of graphs. Yet, the neural scaling laws on graphs, i.e., how the performance of deep …

Content-Aware Point of Interest Recommendation on Location-Based Social Networks

2015 · Proceedings of the AAAI Conference on Artificial Intelligence

The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). POI recommendation is a task that facilitates users' urban exploration and …

Streaming Recommender Systems

2017

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and …

Ranking Relevance in Yahoo Search

2016

Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied …

Recommendation with Social Dimensions

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

The pervasive presence of social media greatly enriches online users' social activities, resulting in abundant social relations. Social relations provide an independent source for recommendation, bringing about new opportunities for recommender systems. Exploiting social relations …

What Your Images Reveal

2017

The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, …

Deep Reinforcement Learning for List-wise Recommendations

2017 · arXiv (Cornell University)

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process …

Micro Behaviors

2018

The explosive popularity of e-commerce sites has reshaped users» shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to observe rich data about users. …

Hierarchical Variational Memory Network for Dialogue Generation

2018

Dialogue systems help various real applications interact with humans in an intelligent natural way. In dialogue systems, the task of dialogue generation aims to generate utterances given previous utterances as contexts. Among various spectrums of …

Deep reinforcement learning for page-wise recommendations

2018

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is - users are recommended a page …

Signed Graph Convolutional Networks

2018

Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and …

Graph Neural Networks for Social Recommendation

2019

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to …

"Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator

2019 · ACM SIGWEB Newsletter

Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time …

Deep social collaborative filtering

2019

Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to …

Jointly Learning to Recommend and Advertise

2020

Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recommending and advertising strategies by different teams separately via different techniques, which …

A Survey on Dialogue Systems

2017 · ACM SIGKDD Explorations Newsletter

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as …