ACM Transactions on the Web最新文献

Web services have been widely used to develop complex distributed software systems in the context of Service Oriented Architecture (SOA). As a standard for describing Web services, the Web Service Description Language (WSDL) provides a universal mechanism to describe the service’s functionalities for the service consumers. However, the current WSDL only provides the description of the interfaces to a Web Service without any restrictions or assumptions on how to properly invoke the service, resulting in divergent understanding of the Web service’s behavior between the service developer and service consumer. A particular challenge is how to make explicit the various behavior assumptions and restrictions of a service (for the user), and make sure that the service implementation conforms to them (for the developer). In this article, we propose a constraint-based model-driven approach to improving the behavior conformance of Web services. In our approach, constraints are introduced in an extended WSDL, called CxWSDL, to formally and explicitly express the implicit restrictions and assumptions on the behavior of a Web service, and then the predefined constraints are used to derive test cases in a model-driven manner to test the service implementation’s conformance to its behavior constraints from the user’s perspective. An empirical study involving four real-life Web services was conducted to evaluate the effectiveness of our approach, and four actual inconsistencies were discovered.

In this article, we provide an overview of ACM TWEB’s special issue, Financial Technology on the Web. This special issue covers diverse topics: (1) a new architecture for leveraging online news to investment and risk management, (2) a cross-platform analysis of the post quality and users’ behaviors, and (3) an empirical study on disentangling decentralized finance compositions. In addition to a guide for the special issue, we also share a brief opinion on the future of financial technology on the Web.

Social media platforms such as Twitter or StockTwits are widely used for sharing stock market opinions between investors, traders, and entrepreneurs. Empirically, previous work has shown that the content posted on these social media platforms can be leveraged to predict various aspects of stock market performance. Nonetheless, actors on these social media platforms may not always have altruistic motivations and may instead seek to influence stock trading behavior through the (potentially misleading) information they post. While a lot of previous work has sought to analyze how social media can be used to predict the stock market, there remain many questions regarding the quality of the predictions and the behavior of active users on these platforms. To this end, this article seeks to address a number of open research questions: Which social media platform is more predictive of stock performance? What posted content is actually predictive, and over what time horizon? How does stock market posting behavior vary among different users? Are all users trustworthy or do some user’s predictions consistently mislead about the true stock movement? To answer these questions, we analyzed data from Twitter and StockTwits covering almost 5 years of posted messages spanning 2015 to 2019. The results of this large-scale study provide a number of important insights among which we present the following: (i) StockTwits is a more predictive source of information than Twitter, leading us to focus our analysis on StockTwits; (ii) on StockTwits, users’ self-labeled sentiments are correlated with the stock market but are only slightly predictive in aggregate over the short-term; (iii) there are at least three clear types of temporal predictive behavior for users over a 144 days horizon: short, medium, and long term; and (iv) consistently incorrect users who are reliably wrong tend to exhibit what we conjecture to be “botlike” post content and their removal from the data tends to improve stock market predictions from self-labeled content.

Stock price movements in financial markets are influenced by large volumes of news from diverse sources on the web, e.g., online news outlets, blogs, social media. Extracting useful information from online news for financial tasks, e.g., forecasting stock returns or risks, is, however, challenging due to the low signal-to-noise ratios of such online information. Assessing the relevance of each news article to the price movements of individual stocks is also difficult, even for human experts. In this article, we propose the Guided Global-Local Attention-based Multimodal Heterogeneous Network (GLAM) model, which comprises novel attention-based mechanisms for multimodal sequential and graph encoding, a guided learning strategy, and a multitask training objective. GLAM uses multimodal information, heterogeneous relationships between companies and leverages significant local responses of individual stock prices to online news to extract useful information from diverse global online news relevant to individual stocks for multiple forecasting tasks. Our extensive experiments with multiple datasets show that GLAM outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management application case-studies.

Conversational Recommendation Systems (CRSs) aim to improve recommendation performance by utilizing information from a conversation session. A CRS first constructs questions and then asks users for their feedback in each conversation session to refine better recommendation lists to users. The key design of CRS is to construct proper questions and obtain users’ feedback in response to these questions so as to effectively capture user preferences. Many CRS works have been proposed; however, they suffer from defects when constructing questions for users to answer: (1) employing a dialogue policy agent for constructing questions is one of the most common choices in CRS, but it needs to be trained with a huge corpus, and (2) it is not appropriate that constructing questions from a single policy (e.g., a CRS only selects attributes that the user has interacted with) for all users with different preferences. To address these defects, we propose a novel CRS model, namely a Representation Fusion–based Conversational Recommendation model, where the whole conversation session is divided into two subsessions (i.e., Local Question Search subsession and Global Question Search subsession) and two different question search methods are proposed to construct questions in the corresponding subsessions without employing policy agents. In particular, in the Local Question Search subsession we adopt a novel graph mining method to find questions, where the paths in the graph between users and attributes can eliminate irrelevant attributes; in the Global Question Search subsession we propose to initialize user preference on items with the user and all item historical rating records and construct questions based on user’s preference. Then, we update the embeddings independently over the two subsessions according to user’s feedback and fuse the final embeddings from the two subsessions for the recommendation. Experiments on three real-world recommendation datasets demonstrate that our proposed method outperforms five state-of-the-art baselines.

The task of Sequential Recommendation aims to predict the user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s own behaviours and dynamic characteristics, while often ignore the high-order collaborative connections when modelling user preferences. Some recent works try to use graph-based methods to introduce high-order collaborative signals for Sequential Recommendation, but they have two main problems. One is that the sequential patterns cannot be effectively mined, and the other is that their way of introducing high-order collaborative signals is not very suitable for Sequential Recommendation. To address these problems, we propose to fully exploit sequence features and model high-order collaborative signals for Sequential Recommendation. We propose a Neighbor user Intention based Sequential Recommender, namely NISRec, which utilizes the intentions of high-order connected neighbor users as high-order collaborative signals, in order to improve recommendation performance for the target user. To be specific, NISRec contains two main modules: the neighbor user intention embedding module (NIE) and the fusion module. The NIE describes both the long-term and the short-term intentions of neighbor users and aggregates them separately. The fusion module uses these two types of aggregated intentions to model high-order collaborative signals in both the embedding process and the user preference modelling phase for recommendation of the target user. Experimental results show that our new approach outperforms the state-of-the-art methods on both sparse and dense datasets. Extensive studies further show the effectiveness of the diverse neighbor intentions introduced by NISRec.