Recommender systems are widely employed to mitigate information overload by tailoring online content to individual preferences. Existing recommendation methods typically focus on optimizing the relevance between candidate item content and user historical behaviors. However, these methods often neglect the quality of recommended content, which can negatively affect user experience and hinder the long-term growth of platforms. In fact, addressing this issue is particularly challenging, as signal on content quality feedback is typically sparse in the user interaction data (e.g., clicks) commonly used for model training. In this article, we propose a human feedback alignment framework for recommender system (HFAR), which leverages well-aligned large language models to simulate human feedback on content quality to enhance recommendation. Specifically, we propose a multi-task learning-based knowledge transfer framework to infuse recommendation models with an awareness of fine-grained feedback on content quality from targeted perspectives. Furthermore, we develop a contrastive learning-based feedback integration mechanism to embed targeted human feedback into the ranking strategy to enable quality-aware recommendation decision-making. Besides, we propose a multi-objective joint training framework to optimize the model jointly under utility and quality objectives. Experiments show that HFAR achieves a maximum improvement of 84.78% in recommendation quality, while maintaining both recommendation accuracy and efficiency.

Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are still many problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation, in this article, we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this article, we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation methods for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in the future. This article can provide up-to-date and comprehensive views on personalized news recommendation. We hope this article can facilitate research on personalized news recommendation as well as related fields in natural language processing and data mining.

Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerable to poisoning attacks by malicious clients. Existing recommender system poisoning methods mainly focus on promoting the recommendation chances of target items due to financial incentives. In fact, in real-world scenarios, the attacker may also attempt to degrade the overall performance of recommender systems. However, existing general FL poisoning methods for degrading model performance are either ineffective or not concealed in poisoning federated recommender systems. In this paper, we propose a simple yet effective and covert poisoning attack method on federated recommendation, named FedAttack. Its core idea is using globally hardest samples to subvert model training. More specifically, the malicious clients first infer user embeddings based on local user profiles. Next, they choose the candidate items that are most relevant to the user embeddings as hardest negative samples, and find the candidates farthest from the user embeddings as hardest positive samples. The model gradients inferred from these poisoned samples are then uploaded for aggregation. Extensive experiments on two benchmark datasets show that FedAttack can effectively degrade the performance of various federated recommender systems, meanwhile cannot be effectively detected nor defended by many existing methods.

News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news. However, each user usually has multiple interests, and it is difficult for these methods to accurately match a candidate news with a specific user interest. In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. We propose a candidate-aware self-attention network that uses candidate news as clue to model candidate-aware global user interest. In addition, we propose a candidate-aware CNN network to incorporate candidate news into local behavior context modeling and learn candidate-aware short-term user interest. Besides, we use a candidate-aware attention network to aggregate previously clicked news weighted by their relevance with candidate news to build candidate-aware user representation. Experiments on real-world datasets show the effectiveness of our method in improving news recommendation performance.

User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level interactions across different clicked news from the same user, which contain rich detailed clues to infer user interest, are ignored by these methods. In this paper, we propose a fine-grained and fast user modeling framework (FUM) to model user interest from fine-grained behavior interactions for news recommendation. The core idea of FUM is to concatenate the clicked news into a long document and transform user modeling into a document modeling task with both intra-news and inter-news word-level interactions. Since vanilla transformer cannot efficiently handle long document, we apply an efficient transformer named Fastformer to model fine-grained behavior interactions. Extensive experiments on two real-world datasets verify that FUM can effectively and efficiently model user interest for news recommendation.

News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behaviors on news. However, these behaviors are usually biased on news providers. Models trained on biased user data may capture and even amplify the biases on news providers, and are unfair for some minority news providers. In this paper, we propose a provider fairness-aware news recommendation framework (named ProFairRec), which can learn news recommendation models fair for different news providers from biased user data. The core idea of ProFairRec is to learn provider-fair news representations and provider-fair user representations to achieve provider fairness. To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data. Provider-fair and -biased news representations are learned from news content and provider IDs respectively, which are further aggregated to build fair and biased user representations based on user click history. All of these representations are used in model training while only fair representations are used for user-news matching to achieve fair news recommendation. Besides, we propose an adversarial learning task on news provider discrimination to prevent provider-fair news representation from encoding provider bias. We also propose an orthogonal regularization on provider-fair and -biased representations to better reduce provider bias in provider-fair representations. Moreover, ProFairRec is a general framework and can be applied to different news recommendation methods. Extensive experiments on a public dataset verify that our ProFairRec approach can effectively improve the provider fairness of many existing methods and meanwhile maintain their recommendation accuracy.

User modeling is critical for personalization. Existing methods usually train user models from task-specific labeled data, which may be insufficient. In fact, there are usually abundant unlabeled user behavior data that encode rich universal user information, and pre-training user models on them can empower user modeling in many downstream tasks. In this paper, we propose a user model pre-training method named UserBERT to learn universal user models on unlabeled user behavior data with two contrastive self-supervision tasks. The first one is masked behavior prediction and discrimination, aiming to model the contexts of user behaviors. The second one is behavior sequence matching, aiming to capture user interest stable in different periods. Besides, we propose a medium-hard negative sampling framework to select informative negative samples for better contrastive pre-training. Extensive experiments validate the effectiveness of UserBERT in user model pre-training.

News recommendation is often modeled as a sequential recommendation task, assuming there are rich short-term dependencies over historical clicked news. However, users usually have strong preferences on the temporal diversity of news information and may not tend to click similar news successively, which is very different from many sequential recommendation scenarios such as e-commerce recommendation. In this paper, we study whether news recommendation can be regarded as a standard sequential recommendation problem. Through extensive experiments on two real-world datasets, we find it suboptimal to model news recommendation as a conventional sequential recommendation problem. To handle this issue, we further propose a temporal diversity-aware sequential news recommendation method that can promote candidate news that are diverse from recently clicked news to help predict future clicks more accurately. Experiments show that our method can empower various news recommendation methods.

News representation is critical for news recommendation. Most existing methods learn news representations only from news texts while ignoring the visual information of news. In fact, users may click news not only due to the interest in news titles but also the attraction of news images. Thus, images are useful for representing news and predicting news clicks. Pretrained visiolinguistic models are powerful in multi-modal understanding, which can represent news from both textual and visual contents. In this paper, we propose a multimodal news recommendation method that can incorporate both textual and visual information of news to learn multimodal news representations. We first extract region-of-interests (ROIs) from news images via object detection. We then use a pre-trained visiolinguistic model to encode both news texts and image ROIs and model their inherent relatedness using co-attentional Transformers. In addition, we propose a crossmodal candidate-aware attention network to select relevant historical clicked news for the accurate modeling of user interest in candidate news. Experiments validate that incorporating multimodal news information can effectively improve the performance of news recommendation.

Accurate user interest modeling is important for news recommendation. Most existing methods for news recommendation rely on implicit feedbacks like click for inferring user interests and model training. However, click behaviors usually contain heavy noise, and cannot help infer complicated user interest such as dislike. Besides, the feed recommendation models trained solely on click behaviors cannot optimize other objectives such as user engagement. In this paper, we present a news feed recommendation method that can exploit various kinds of user feedbacks to enhance both user interest modeling and model training. We propose a unified user modeling framework to incorporate various explicit and implicit user feedbacks to infer both positive and negative user interests. In addition, we propose a strong-to-weak attention network that uses the representations of stronger feedbacks to distill positive and negative user interests from implicit weak feedbacks for accurate user interest modeling. Besides, we propose a multi-feedback model training framework to learn an engagement-aware feed recommendation model. Extensive experiments on a real-world dataset show that our approach can effectively improve the model performance in terms of both news clicks and user engagement.

Native ad is a popular type of online advertisement that has similar forms with the native content displayed on websites. Native ad click-through rate (CTR) prediction is useful for improving user experience and platform revenue. However, it is challenging due to the lack of explicit user intent, and user behaviors on the platform with native ads may be insufficient to infer users’ interest in ads. Fortunately, user behaviors exist on many online platforms that can provide complementary information for user-interest mining. Thus, leveraging multi-platform user behaviors is useful for native ad CTR prediction. However, user behaviors are highly privacy-sensitive, and the behavior data on different platforms cannot be directly aggregated due to user privacy concerns and data protection regulations. Existing CTR prediction methods usually require centralized storage of user behavior data for user modeling, which cannot be directly applied to the CTR prediction task with multi-platform user behaviors. In this article, we propose a federated native ad CTR prediction method named FedCTR, which can learn user-interest representations from cross-platform user behaviors in a privacy-preserving way. On each platform a local user model learns user embeddings from the local user behaviors on that platform. The local user embeddings from different platforms are uploaded to a server for aggregation, and the aggregated ones are sent to the ad platform for CTR prediction. Besides, we apply local differential privacy and differential privacy to the local and aggregated user embeddings, respectively, for better privacy protection. Moreover, we propose a federated framework for collaborative model training with distributed models and user behaviors. Extensive experiments on real-world dataset show that FedCTR can effectively leverage multi-platform user behaviors for native ad CTR prediction in a privacy-preserving manner.

The most important task in personalized news recommendation is accurate matching between candidate news and user interest. Most of existing news recommendation methods model candidate news from its textual content and user interest from their clicked news in an independent way. However, a news article may cover multiple aspects and entities, and a user usually has different kinds of interest. Independent modeling of candidate news and user interest may lead to inferior matching between news and users. In this paper, we propose a knowledge-aware interactive matching method for news recommendation. Our method interactively models candidate news and user interest to facilitate their accurate matching. We design a knowledge-aware news co-encoder to interactively learn representations for both clicked news and candidate news by capturing their relatedness in both semantic and entities with the help of knowledge graphs. We also design a user-news co-encoder to learn candidate news-aware user interest representation and user-aware candidate news representation for better interest matching. Experiments on two real-world datasets validate that our method can effectively improve the performance of news recommendation.

Personalized news recommendation is an essential technique for online news services. News articles usually contain rich textual content, and accurate news modeling is important for personalized news recommendation.

With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Existing news recommendation methods achieve personalization by building accurate news representations from news content and user representations from their direct interactions with news (e.g., click), while ignoring the high-order relatedness between users and news. Here we propose a news recommendation method which can enhance the representation learning of users and news by modeling their relatedness in a graph setting. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors. For news representations, a transformer architecture is first exploited to build news semantic representations. Then we combine it with the information from neighbor news in the graph via a graph attention network. For user representations, we not only represent users from their historically clicked news, but also attentively incorporate the representations of their neighbor users in the graph. Improved performances on a large-scale real-world dataset validate the effectiveness of our proposed method.

Estimating in-hospital costs from medical records is an important task with many applications such as accountable care. Existing methods for this task usually rely on manual feature engineering which needs massive domain knowledge, and do not exploit the textual information in medical records, e.g., diagnosis and operation texts. In this paper, we propose a neural in-hospital cost estimation (NICE) approach to estimate the in-hospital costs of patients from their admission records. Our approach can exploit the heterogeneous information in records, such as patient features, diagnosis/operation texts, and the diagnosis/operation IDs, via a multi-view learning framework. In addition, since different words, diagnoses and operations have different importance for cost estimation, we propose a hierarchical attention network to select important words, diagnoses and operations for learning informative record representations. Extensive experiments on a real-world medical dataset validate the effectiveness of our approach.

Sentiment classification is an important task in the sentiment analysis field. Many deep learning based sentiment classification methods have been proposed in recent years. However, these methods usually rely on massive labeled texts to train sentiment classifiers, which are expensive and time-consuming to annotate. Luckily, many high-quality sentiment lexicons have been constructed and can cover a large number of sentiment words. Since sentiment words are the basic units to convey sentiments in texts, these sentiment lexicons have the potential to improve the performance of neural sentiment classification. In this paper, we propose two approaches to exploit sentiment lexicons to enhance neural sentiment classification. In our first approach we use sentiment lexicons to learn sentiment-aware attentions. We propose a word sentiment classification task to classify the sentiments of words in a sentence based on their hidden representations in the attention network of neural sentiment classification models. We jointly train this task with neural sentiment classifier to facilitate the attention network to recognize and highlight sentiment-bearing words. In our second approach we use sentiment lexicons to learn sentiment-aware word embeddings. We design an auxiliary task to classify the sentiments of words in sentiment lexicons based on their word embeddings, and jointly train this task with neural sentiment classifier to encode sentiment information in sentiment lexicons to word embeddings. Extensive experiments on three benchmark datasets validate the effectiveness of our approach.

Review rating prediction is an important task in data mining and natural language processing fields, and has wide applications. Users usually express opinions towards many aspects in their reviews, and the overall review rating is a synthesis of these opinions. However, most existing review rating prediction methods ignore users' opinions on aspects, which is insufficient. In this paper, we propose a neural aspect-aware rating prediction approach for Chinese reviews. In our approach we propose a collaborative learning framework to jointly train review-level rating predictor and multiple aspect-level rating predictors. In our framework different rating predictors share the same review encoder model to exploit the inherent relatedness between them, but have different attention networks to focus on different informative texts for each task. The final review representation for rating prediction is a concatenation of the review representations from all predictors. Since word segmentation of Chinese reviews is usually inaccurate, we propose a multi-view learning model to learn review representations from both words and characters. Extensive experiments on real-world dataset validate the effectiveness of our approach.

Demographics of social media users such as gender are very important for personalized online services. However, the gender information of many users is usually not available. Luckily, the messages posted by social media users can provide rich clues for inferring their genders, since male and female users usually have differences in their message content. In addition, users with different genders often have different patterns in expressing emotions. In this paper, we propose a neural approach for gender prediction in social media based on both content and emotion of messages posted by users. The core of our approach is an emotion-aware hierarchical user representation model. Our model first learns message representations from words using message encoder and then learns user representations from messages using user encoder with hierarchical attention networks selecting important words and messages to learn informative user representations. In addition, we propose two methods to incorporate emotion information in messages into user representation learning. The first one is to incorporate emotion-aware message representations generated by a pre-trained emotion classifier into message representations. The second one is to train emotion-aware message encoders via jointly training our model with an auxiliary emotion classification task. Extensive experiments on two real-world datasets validate the effectiveness of our approach.

Neural network methods have achieved great success in sentiment classification. Recent studies have found that incorporating user and product information can effectively improve the performance of review sentiment classification. However, most of these studies only concentrate on the influence of users and products, ignoring the inherent correlation between users or products. This information is important for users or products since they can obtain more information from similar users or products. In this paper, we propose a novel framework for review rating prediction with user and product memory. First, besides the original user or product representations, we construct inferred representations from representative users or products which are stored in memory slots. These memory units can be viewed as refined knowledge representations of users or products learned from the data. Then, we employ two hierarchical networks with user attention and product attention using both the original and inferred representations. Experiments on benchmark datasets show that our method can achieve state-of-the-art performance. Besides, our approach performs much more better in cold-start scenarios where the training data is scarce.

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word- and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.

Demographics of online users such as age and gender play an important role in personalized web applications. However, it is difficult to directly obtain the demographic information of online users. Luckily, search queries can cover many online users and the search queries from users with different demographics usually have some difference in contents and writing styles. Thus, search queries can provide useful clues for demographic prediction. In this paper, we study predicting users' demographics based on their search queries, and propose a neural approach for this task. Since search queries can be very noisy and many of them are not useful, instead of combining all queries together for user representation, in our approach we propose a hierarchical user representation with attention (HURA) model to learn informative user representations from their search queries. Our HURA model first learns representations for search queries from words using a word encoder, which consists of a CNN network and a word-level attention network to select important words. Then we learn representations of users based on the representations of their search queries using a query encoder, which contains a CNN network to capture the local contexts of search queries and a query-level attention network to select informative search queries for demographic prediction. Experiments on two real-world datasets validate that our approach can effectively improve the performance of search query based age and gender prediction and consistently outperform many baseline methods.

Twitter is a popular social media platform for information sharing and dissemination. Many Twitter users post tweets to share their experiences about drugs and adverse drug reactions. Automatic detection of tweets mentioning drug names and adverse drug reactions at a large scale has important applications such as pharmacovigilance. However, detecting drug name and adverse drug reaction mentioning tweets is very challenging, because tweets are usually very noisy and informal, and there are massive misspellings and user-created abbreviations for these mentions. In addition, these mentions are usually context dependent. In this paper, we propose a neural approach with hierarchical tweet representation and multi-head self-attention mechanism to jointly detect tweets mentioning drug names and adverse drug reactions. In order to alleviate the influence of massive misspellings and user-created abbreviations in tweets, we propose to use a hierarchical tweet representation model to first learn word representations from characters and then learn tweet representations from words. In addition, we propose to use multi-head self-attention mechanism to capture the interactions between words to fully model the contexts of tweets. Besides, we use additive attention mechanism to select the informative words to learn more informative tweet representations. Experimental results validate the effectiveness of our approach.

Chinese word segmentation (CWS) is very important for Chinese text processing. Existing methods for CWS usually rely on a large number of labeled sentences to train word segmentation models, which are expensive and time-consuming to annotate. Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS. In this paper, we propose a neural approach for Chinese word segmentation which can exploit both lexicon and unlabeled data. Our approach is based on a variant of posterior regularization algorithm, and the unlabeled data and lexicon are incorporated into model training as indirect supervision by regularizing the prediction space of CWS models. Extensive experiments on multiple benchmark datasets in both in-domain and cross-domain scenarios validate the effectiveness of our approach.

Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly context-dependent. In addition, Chinese texts lack delimiters to separate words, making it difficult to identify the boundary of entities. Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming. In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER. Second, we propose a unified framework to jointly train CNER and word segmentation models in order to enhance the ability of CNER model in identifying entity boundaries. Third, we introduce an automatic method to generate pseudo labeled samples from existing labeled data which can enrich the training data. Experiments on two benchmark datasets show that our approach can effectively improve the performance of Chinese named entity recognition, especially when training data is insufficient.

Sentence-level sentiment classification aims to mine fine-grained sentiment information from texts. Existing methods for this task are usually based on supervised learning and rely on massive labeled sentences for model training. However, annotating sufficient sentences is expensive and time-consuming. In this paper, we propose a neural sentence-level sentiment classification approach which can exploit heterogeneous sentiment supervision and reduce the dependence on labeled sentences. Besides the sentence-level supervision from labeled sentences, our approach can also incorporate the word-level supervision extracted from sentiment lexicons, document-level supervision extracted from labeled documents and sentiment relations between sentences extracted from unlabeled documents. A unified neural framework is proposed to fuse heterogeneous sentiment supervision to train sentence-level sentiment classification model. Experiments on benchmark datasets validate the effectiveness of our approach.

Sentence-level sentiment classification is important to understand users' fine-grained opinions. Existing methods for sentence-level sentiment classification are mainly based on supervised learning. However, it is difficult to obtain sentiment labels of sentences since manual annotation is expensive and time-consuming. In this paper, we propose an approach for sentence-level sentiment classification without the need of sentence labels. More specifically, we propose a unified framework to incorporate two types of weak supervision, i.e., document-level and word-level sentiment labels, to learn the sentence-level sentiment classifier. In addition, the contextual information of sentences and words extracted from unlabeled sentences is incorporated into our approach to enhance the learning of sentiment classifier. Experiments on benchmark datasets show that our approach can effectively improve the performance of sentence-level sentiment classification.

We propose a collaborative multi-domain sentiment classification approach to train sentiment classifiers for multiple domains simultaneously. In our approach, the sentiment information in different domains is shared to train more accurate and robust sentiment classifiers for each domain when labeled data is scarce. Specifically, we decompose the sentiment classifier of each domain into two components, a global one and a domain-specific one. The global model can capture the general sentiment knowledge and is shared by various domains. The domain-specific model can capture the specific sentiment expressions in each domain. In addition, we extract domain-specific sentiment knowledge from both labeled and unlabeled samples in each domain and use it to enhance the learning of domain-specific sentiment classifiers. Besides, we incorporate the similarities between domains into our approach as regularization over the domain-specific sentiment classifiers to encourage the sharing of sentiment information between similar domains. Two kinds of domain similarity measures are explored, one based on textual content and the other one based on sentiment expressions. Moreover, we introduce two efficient algorithms to solve the model of our approach. Experimental results on benchmark datasets show that our approach can effectively improve the performance of multi-domain sentiment classification and significantly outperform baseline methods.

Sentiment domain adaptation is widely studied to tackle the domain-dependence problem in sentiment analysis field. Existing domain adaptation methods usually train a sentiment classifier in a source domain and adapt it to the target domain using transfer learning techniques. However, when the sentiment feature distributions of the source and target domains are significantly different, the adaptation performance will heavily decline. In this paper, we propose a new sentiment domain adaptation approach by adapting the sentiment knowledge in general-purpose sentiment lexicons to a specific domain. Since the general sentiment words of general-purpose sentiment lexicons usually convey consistent sentiments in different domains, they have better generalization performance than the sentiment classifier trained in a source domain. In addition, we propose to extract various kinds of contextual sentiment knowledge from massive unlabeled samples in target domain and formulate them as sentiment relations among sentiment expressions. It can propagate the sentiment information in general sentiment words to massive domain-specific sentiment expressions. Besides, we propose a unified framework to incorporate these different kinds of sentiment knowledge and learn an accurate domain-specific sentiment classifier for target domain. Moreover, we propose an efficient optimization algorithm to solve the model of our approach. Extensive experiments on benchmark datasets validate the effectiveness and efficiency of our approach.

The popularity of microblogging platforms, such as Twitter, makes them important for information dissemination and sharing. However, they are also recognized as ideal places by spammers to conduct social spamming. Massive social spammers and spam messages heavily hurt the user experience and hinder the healthy development of microblogging systems. Thus, effectively detecting the social spammers and spam messages in microblogging is of great value. Existing studies mainly regard social spammer detection and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high probabilities to be posted by social spammers. Combining social spammer detection with spam message detection has the potential to boost the performance of each task. In this paper, we propose a unified framework for social spammer and spam message co-detection in microblogging. Our framework utilizes the posting relations between users and messages to combine social spammer detection with spam message detection. In addition, we extract the social relations between users as well as the connections between messages, and incorporate them into our framework as regularization terms over the prediction results. Besides, we introduce an efficient optimization method to solve our framework. Extensive experiments on a real-world microblog dataset demonstrate that our framework can significantly and consistently improve the performance of both social spammer detection and spam message detection.

Sentiment classification is a hot research topic in both industrial and academic fields. The mainstream sentiment classification methods are based on machine learning and treat sentiment classification as a text classification problem. However, sentiment classification is widely recognized as a highly domain-dependent task. The sentiment classifier trained in one domain may not perform well in another domain. A simple solution to this problem is training a domain-specific sentiment classifier for each domain. However, it is difficult to label enough data for every domain since they are in a large quantity. In addition, this method omits the sentiment information in other domains. In this paper, we propose to train sentiment classifiers for multiple domains in a collaborative way based on multi-task learning. Specifically, we decompose the sentiment classifier in each domain into two components, a general one and a domain-specific one. The general sentiment classifier can capture the global sentiment information and is trained across various domains to obtain better generalization ability. The domain-specific sentiment classifier is trained using the labeled data in one domain to capture the domain-specific sentiment information. In addition, we explore two kinds of relations between domains, one based on textual content and the other one based on sentiment word distribution. We build a domain similarity graph using domain relations and encode it into our approach as regularization over the domain-specific sentiment classifiers. Besides, we incorporate the sentiment knowledge extracted from sentiment lexicons to help train the general sentiment classifier more accurately. Moreover, we introduce an accelerated optimization algorithm to train the sentiment classifiers efficiently. Experimental results on two benchmark sentiment datasets show that our method can outperform baseline methods significantly and consistently.

Correlated topical trend detection is very useful in analyzing public and social media influence. In this paper, we propose an algorithm that can both detect the correlation and discover the corresponding keywords that trigger the correlation. To detect the correlation, we use a projection vector to project two text streams onto the same space, and then use a least square cost function to regress one text stream over the other with different time lags. To extract the corresponding keywords, we impose the non-negative sparsity constraints over the projection parameters. In addition, we present an accelerated algorithm based on Nesterov's method to efficiently solve the optimization problem. In our experiments, we use both syntehtic and real data sets to demonstrate the advantages and capabilities of the proposed algorithm over CCA on the follower link prediction problem.