Guoshuai Zhao

Xi'an Jiaotong University | XJTU ·  School of Software Engineering

Nowadays, with the boom of social media and e-commerce, more and more people prefer to share their consumption experiences and rate services on review sites. Much research has focused on personalized recommendation. However, quality of service also plays an important role in recommender systems, and it is the main concern of this paper. An overall...

With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before p...

Trip is an important part of people’s lives. We need to obtain interesting location information quickly and conveniently from the huge amount of information. The existing data source of geographic locations can be divided into two categories. The one is users’ information that we can utilize to deeply understand their trajectories, the other one is...

Recently, advances in intelligent mobile device and positioning techniques have fundamentally enhanced social networks, which allows users to share their experiences, reviews, ratings, photos, check-ins, etc. The geographical information located by smart phone bridges the gap between physical and digital worlds. Location data functions as the conne...

In recent years, we have witnessed a flourish of review websites. It presents a great opportunity to share our viewpoints for various products we purchase. However, at the same time we face the information overloading problem. How to mine valuable information from reviews to understand a user’s preferences and make an accurate recommendation is cru...

Medical decision making often relies on accurately forecasting future patient trajectories. Conventional approaches for patient progression modeling often do not explicitly model treatments when predicting patient trajectories and outcomes. In this paper, we propose Alternating Transformer (AL-Transformer) to jointly model treatment dynamics and cl...

The high rate of false arrhythmia alarms in intensive care units (ICUs) can negatively impact patient care and lead to slow staff response time due to alarm fatigue. To reduce false alarms in ICUs, previous works proposed conventional supervised learning methods which have inherent limitations in dealing with high-dimensional, sparse, unbalanced, a...

Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The key of this task is effective knowledge transfer from the base session to the incremental sessions. Despite the advance of existing FSCIL methods, the proposed knowledge transfer learning schemes are sub-op...

Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are limited by the circumscribed logical paradigms of training samples, which leads to a weak generalization of unseen logic. To address these issues, we propose a plug-in modu...

In image-text matching fields, one of the keys to improving performance is to extract features with more semantic information. Existing works demonstrate that semantic enrichment through knowledge expansion can improve performance. Most of them expand image features, however, the shortage of semantic information in text modality and the unilateral...

Semantic text similarity (STS), which measures the semantic similarity of sentences, is an important task in the field of NLP. It has a wide range of applications, such as machine translation (MT), semantic search, and summarization. In recent years, with the development of deep neural networks, the existing semantic similarity measurement has made...

italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The scarcity of new training data will seriously destroy the model’s stability and plasticity. Continually Evolv...

AI-based methods are shining across a variety of industries, especially unmanned retail. Product recognition is the problem of recognizing the category and quantity of products (e.g., beverages and mineral water) in intelligent unmanned vending machines (UVMs) to automatic checkout during purchase. However, for similar products in hundreds of categ...

Existing point-of-interest (POI) recommendation methods only show the direct recommendation results and lack the proper reasons for recommendation. In recent years, explainable recommendation has become an increasingly important subfield in recommendation systems. The aim of explainable recommendation is to provide a reason why an item is recommend...

The recommendation system is fundamental technology of the internet industry intended to solve the information overload problem in the big data era. Top-k recommendation is an important task in this field. It generally functions through the comparison of positive pairs and negative pairs based on Bayesian personalized ranking (BPR) loss. We find th...

Recently, the emerging concept of "unmanned retail" has drawn more and more attention, and the unmanned retail based on the intelligent unmanned vending machines (UVMs) scene has great market demand. However, existing product recognition methods for intelligent UVMs cannot adapt to large-scale categories and have insufficient accuracy. In this arti...

The high rate of false arrhythmia alarms in Intensive Care Units (ICUs) can lead to disruption of care, negatively impacting patients’ health through noise disturbances, and slow staff response time due to alarm fatigue. Prior false-alarm reduction approaches are often rule-based and require hand-crafted features from physiological waveforms as inp...

With the rapid development of social media and big data technology, user’s sequence behavior information can be well recorded and preserved on different media platforms. It is crucial to model the user preference through mining their sequential behaviors. The goal of sequential recommendation is to predict what a user may interact with in the next...

Most recommendation systems focus on predicting rating or finding aspect information in reviews to understand user preferences and item properties. However, these methods ignore the effectiveness and persuasiveness of recommendation results. Consequently, explainable recommendation, namely providing recommendation results with recommendation reason...

Dating recommendation becomes a critical task since the rapid development of online dating sites and it is beneficial for users to find their ideal relationships from a large number of registered members. Different users usually have different tastes when choosing their dating partners. Therefore, it is necessary to distinguish the users personal f...

Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users' dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negl...

Top-k recommendation is a fundamental task in recommendation systems that is generally learned by comparing positive and negative pairs. The contrastive loss (CL) is the key in contrastive learning that has recently received more attention, and we find that it is well suited for top-k recommendations. However, CL is problematic because it treats th...

The top-k recommendation is a fundamental task in recommendation systems which is generally learned by comparing positive and negative pairs. The Contrastive Loss (CL) is the key in contrastive learning that has received more attention recently and we find it is well suited for top-k recommendations. However, it is a problem that CL treats the impo...

Sequential recommendations aim to predict the users next behaviors items based on their successive historical behaviors sequence. It has been widely applied in lots of online services. However, current sequential recommendations use the adjacent behaviors to capture the features of the sequence, ignoring the features among nonadjacent sequential it...

The recommendation system is an important and widely used technology in the era of Big Data. Current methods have fused side information into it to alleviate the sparsity problem, one of the key problems of recommendation systems. However, not all the side information can be obtained with high quality, and the specific methods based on side informa...

Person re-identification (re-ID) has made substantial progress in recent years; however, it is still challenging to search for the target person in a short time. Re-ID with deep hashing is a shortcut for that but, limited by the expression of binary code, the performance of the hashing method is not satisfactory. Besides, to further speed up retrie...

Making phone calls, sending messages and surfing the Internet all depend on wireless communication. Too many users connect to a same base station at the same time, which would slow network speed down. To address this issue, telecom operators can tune the network capacity in advance according to predicted Maximum Connections. Therefore, predicting M...

Deep multi-view subspace clustering has achieved promising performance compared with other multi-view clustering. However, existing deep multi-view subspace clustering only considers the global structure for all views, and they ignore the local geometric structure among each view. In addition, they cannot learn discriminative feature on different c...

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model both users' general taste and recent sequential behavior. Moreover, different users show different dependencies on the two parts. However, most ex...

Partial differential equations (PDEs) are essential foundations to model dynamic processes in natural sciences. Discovering the underlying PDEs of complex data collected from real world is key to understanding the dynamic processes of natural laws or behaviors. However, both the collected data and their partial derivatives are often corrupted by no...

Internet users would like to obtain interesting location information for a travel. With the rapid development of social media, many kinds of location recommender systems are proposed in recent years. Existing methods mostly focus on mining user check-in information that could be leveraged to understand their trajectories. However, the characteristi...

The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the higher storage requirement and computational cost of retraining previous tasks with MTMV models have pre...

With the popularity of social platforms, emoji appears and becomes extremely popular with a large number of users. It expresses more beyond plaintexts and makes the content more vivid. Using appropriate emojis in messages and microblog posts makes you lovely and friendly. Recently, emoji recommendation becomes a significant task since it is hard to...

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to...

Recently, more and more people have the preference for obtaining the latest news and posting their views relying on social media. In this way, some opinion leaders would ultimately get a large number of followers. Because of the significant influence imposed by their social accounts, some of them start to post native advertisements in their article...

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as "Customers who bought this item also bought...". Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. Th...

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as "Customers who bought this item also bought...". Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. Th...

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as "Customers who bought this item also bought...". Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. Th...

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as "Customers who bought this item also bought...". Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. Th...

Personalized recommendation has received a lot of attention as a highly practical research topic. However, existing recommender systems provide the recommendations with a generic statement such as "Customers who bought this item also bought...". Explainable recommendation, which makes a user aware of why such items are recommended, is in demand. Th...

With the development of e-commerce, shopping on-line is becoming more and more popular. The explosion of reviews have led to a serious problem, information overloading. How to mine user interest from these reviews and understand users’ preference is crucial for us. Traditional recommender systems mainly use structured data to mine user interest pre...

Effective location recommendation is an important problem in both research and industry. Much research has focused on personalized recommendation for users. However, there are more uses such as site selection for firms and factories. In this study, we try to solve site selection problem by recommending some locations satisfying special requirements...

With the boom of social media, it is a very popular trend for people to share their consumption experiences and rate the items on the review site. Users can share their experiences, reviews, ratings, photos, check-ins, moods, and so on. The information they shared is valuable for new users to judge whether the items have high-quality services. Nowa...

With the boom of e-commerce, it is a very popular trend for people to share their consumption experience and rate the items on a review site. The information they shared is valuable for new users to judge whether the items have high-quality services. Nowadays, many researchers focus on personalized recommendation and rating prediction. They miss th...

With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity proble...

With the popularity and rapid development of social network, more and more people enjoy sharing their experiences, such as reviews, ratings and moods. And there are great opportunities to solve the cold start and sparse data problem with the new factors of social network like interpersonal influence and interest based on circles of friends. Some al...