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Utilizing the demographic information of social media users is very essential for personalized online services. However, it is difficult to collect such information in most realistic scenarios. Luckily, the reviews posted by users can provide rich clues for inferring their demographics, since users with different demographics such as gender and age usually have differences in their contents and expressing styles. In this paper, we propose a neural approach for demographic prediction based on user reviews. The core of our approach is a deep multi-view multi-task learning model. Our model first learns context representations from reviews using a context encoder, which takes semantics and syntactics into consideration. Meanwhile, we learn sentiment and topic representations from selected sentiment and topic words using a word encoder separately, which consists of a convolutional neural network to capture the local contexts of reviews in word-level. Then, we learn a unified user representation from context, sentiment and topic representations and apply multi-task learning for inferring user’s gender and age simultaneously. Experimental results on three real-world datasets validate the effectiveness of our approach. To facilitate future research, we release the codes and datasets at https://github.com/icmpnorequest/DASFAA2021_DMVMT.

Point-of-interest (POI) recommendation has become an important service to help users discover attractive locations. A variety of available check-in data make it possible to build a personalized POI recommender system, but the extreme sparsity of check-in data poses a severe challenge for POI recommendation. Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data. However, these studies often apply shallow methods for the extra information and provide considerably limited improvements on POI recommendation. In this paper, we propose a fine-grained POI recommendation framework, called FGRec to capture the intrinsic influences of social, categorical and geographical information on the check-in behaviors of users. First, we study the social influence in depth by exploiting the multi-hop social friends and top-n nearest neighbor friends, not only the direct friends (i.e., 1-hop friends). Second, we investigate the categorical influence by factorizing both user-POI and user-category matrices simultaneously over the same user embedding space, rather than simply using the popularity of POI categories. Third, we explore the geographical influence by integrating two types of distance (i.e., the distance between user homes and POIs and the distance among POIs) into a unified probability distribution over check-in POIs, instead of modeling them separately. Finally, experimental results on two large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

User Alignment is to find users belonging to a same real person on different social networks and has become a fundamental task for many sequent applications such as cross-network recommendation systems. When matching users in multiple social networks, existing approaches always know some correctly matched users, which can be called seeds. Then, existing methods strongly depend on the neighboring users of each user to propagate alignment information from seeds and align probable matching users implicitly. However, the completeness and validity of original alignment information among seeds cannot be fully preserved when learning and aligning multiple user spaces. In this paper, we propose a unified framework named Jumping Seed Alignment Information Propagation (JSAIP) to flexibly leverage, for each user, complete and correct alignment information from seeds. Specifically, JSAIP learns a reasonable user space for each social network by preserving enough original network and label information. Then, JSAIP ensures the correct alignment among seeds and shared labels to reduce the diversity between different user spaces. Finally, JSAIP constructs jumping links from seeds to each user in each social network and ultilizes original seed alignment information to enhance or rectify the alignment information propagated from neighbors. Experiments on real world datasets demonstrate the effectiveness of our proposed JSAIP method compared to several state-of-the-art methods.

There is a significant style overfitting problem in neural-based character recognition: insufficient generalization ability to recognize characters with unseen styles. To address this problem, we propose a novel framework named Style-Melt Nets (SMN), which disentangles the style and content factors to extract pure content feature. In this framework, a pair of parallel style net and content net is designed to respectively infer the style labels and content labels of input character images, and the style feature produced by the style net is fed to the content net for eliminating the style influence on content feature. In addition, the marginal distribution of character pixels is considered as an important structure indicator for enhancing the content representations. Furthermore, to increase the style diversity of training data, an efficient data augmentation approach for changing the thickness of the strokes and generating outline characters is presented. Extensive experimental results demonstrate the benefit of our methods, and the proposed SMN is able to achieve the state-ofthe-art performance on multiple real world character sets.

Demographic attributes are crucial for characterizing different types of users in developing market strategy. However, in retail scenario, individual demographic information is not often available due to the difficult manual collection process. Several studies focus on inferring users’ demographic attribute based on their transaction histories, but there is a common problem. Hardly work has introduced knowledge for purchase data embedding. Specifically, purchase data is informative, full of related knowledge entities and common sense. However, existing methods are unaware of such external knowledge and latent knowledge-level connections among items. To address the above problem, we propose a Knowledge-Aware Embedding (KAE) method that incorporates knowledge graph representation into demographic prediction. The KAE is a multi-channel and item-entity-aligned knowledge-aware convolutional neural network that fuses frequency-level and knowledge-level representations of purchase data. Through extensive experiments on a real world dataset, we demonstrate that KAE achieves substantial gains on state-of-the-art demographic prediction models.

User Identity Linkage is to find users belonging to the same real person in different social networks. Besides, anchor users refer to matching users known in advance. However, how to match users only based on network information is still very difficult and existing embedding methods suffer from the challenge of error propagation. Error propagation means the error occurring in learning some users’ embeddings may be propagated and amplified to other users along with edges in the network. In this paper, we propose the Anchor UseR ORiented Accordant Embedding (AURORAE) method to learn the vector representation for each user in each social network by capturing useful network information and avoiding error propagation. Specifically, AURORAE learns the potential relations between anchor users and all users, which means each user is directly connected to all anchor users and the error cannot be propagated without paths. Then, AURORAE captures the useful local structure information into final embeddings under the constraint of accordant vector representations between anchor users. Experimental results on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.

There is a significant style overfitting problem in traditional content supervision models of character recognition: insufficient generalization ability to recognize the characters with unseen font styles. To overcome this problem, in this paper we propose a novel framework named Style and Content Supervision (SCS) network, which integrates style and content supervision to resist style overfitting. Different from traditional models only supervised by content labels, SCS simultaneously leverages the style and content supervision to separate the task-specific features of style and content, and then mixes the style-specific and content-specific features using bilinear model to capture the hidden correlation between them. Experimental results prove that the proposed model is able to achieve the state-of-the-art performance on several widely used real world character sets, and it obtains relatively strong robustness when the size of training set is shrinking.

POI (point-of-interest) recommendation as an important location-based service has been widely utilized in helping people discover attractive locations. A variety of available check-in data provide a good opportunity for developing personalized POI recommender systems. However, the extreme sparsity of check-in data and inefficiency of exploiting unobserved feedback pose severe challenges for POI recommendation. To cope with these challenges, we develop a heterogeneous graph embedding-based personalized POI recommendation framework called HRec. It consists of two modules: the learning module and the ranking module. Specifically, we first propose the learning module to produce a series of intermediate feedback from unobserved feedback by learning the embeddings of users and POIs in the heterogeneous graph. Then we devise the ranking module to recommend each user the ultimate ranked list of relevant POIs by utilizing two pairwise feedback comparisons. Experimental results on two real-world datasets demonstrate the effectiveness and superiority of the proposed method.

Aligning users belonging to the same person in different social networks has attracted much attention. Recently, embedding methods have been proposed to represent users from different social networks into vector spaces with same dimension. To handle the challenge of vector space diversity, existing methods usually make vectors of known aligned users closer/consistent and overlap different vector spaces. However, compared to large amount of users in each social network, the consistence constraint on aligned users is not enough to reduce the diversity. Besides, missing edges/labels may provide incorrect information and affect the effect of the overlap between learned vector spaces. Therefore, we propose the OURLACER method, i.e, jOint UseR and LAbel ConsistencE Representation, to jointly represent each user and label under the consistence constraints of know aligned users and shared labels. Specifically, OURLACER utilizes collective matrix factorization to complete missing edges and labels for each user, which can provide sufficient information to distinguish each user. Moreover, OURLACER adds the consistence constraint on shared labels in different social networks. Because each user has own labels, label consistence can restrict each user and greatly reduce the diversity between learned vector spaces. Extensive experiments conducted on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.

With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.

User identity linkage is to identify all the users belonging to the same individual in different networks and has been widely studied along with the increasing popularity of diverse social media sites. Generally, a pair of probable corresponding users on different networks may form a true “Anchor Link”. Most existing methods identify a user based on unique features (username, interests, friends, etc.) and neglect the importance of users local network structure. Therefore, one challenging problem is how to address the user identity linkage problem if only structural information is available. In this paper, we explore techniques for dealing with the fundamental and accumulated information from neighbouring anchor links. Furthermore, we design a Trustworthy Predicting Approach (TPA) for computing the authority of an anchor link, inferring the trustworthiness of a candidate anchor link being true and predicting whether an anchor link is able to be veritably formed. Experiments illustrate the effectiveness of our proposed algorithm.

Recently, the convolutional neural networks (CNNs) show the great power in dealing with various image classification tasks. However, in the task of Chinese character recognition, there is a significant problem in CNN-based classifiers: insufficient generalization ability to recognize the Chinese characters with unfamiliar font styles. We call this problem the Style Overfitting. In the process of a human recognizing Chinese characters with various font styles, the internal skeletons of these characters are important indicators. This paper proposes a novel tool named Skeleton Kernel to capture skeleton features of Chinese characters. And we use it to assist CNN-based classifiers to prevent the Style Overfitting problem. Experimental results prove that our method firmly enhances the generalization ability of CNN-based classifiers. And compared to previous works, our method requires a small training set to achieve relatively better performance.