711 research outputs found

    A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

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    In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

    Management of Big Annotations in Relational Database Management Systems

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    Annotations play a key role in understanding and describing the data, and annotation management has become an integral component in most emerging applications such as scientific databases. Scientists need to exchange not only data but also their thoughts, comments and annotations on the data as well. Annotations represent comments, Lineage of data, description and much more. Therefore, several annotation management techniques have been proposed to efficiently and abstractly handle the annotations. However, with the increasing scale of collaboration and the extensive use of annotations among users and scientists, the number and size of the annotations may far exceed the size of the original data itself. However, current annotation management techniques don’t address large scale annotation management. In this work, we propose three chapters to that tackle the Big annotations from three different perspectives (1) User-Centric Annotation Propagation, (2) Proactive Annotation Management and (3) InsightNotes Summary-Based Querying. We capture users\u27 preferences in profiles and personalizes the annotation propagation at query time by reporting the most relevant annotations (per tuple) for each user based on time plan. We provide three Time-Based plans, support static and dynamic profiles for each user. We support a proactive annotation management which suggests data tuples to be annotated in case new annotation has a reference to a data value and user doesn’t annotate the data precisely. Moreover, we provide an extension on the InsightNotes: Summary-Based Annotation Management in Relational Databases by adding query language that enable the user to query the annotation summaries and add predicates on the annotation summaries themselves. Our system is implemented inside PostgreSQL

    TF-IDF Based Contextual Post-Filtering Recommendation Algorithm in Complex Interactive Situations of Online to Offline: An Empirical Study

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    O2O accelerates the integration of online and offline, promotes the upgrading of industrial structure and consumption pattern, meanwhile brings the information overload problem. This paper develops a post-context filtering recommendation algorithm based on TF-IDF, which improves the existing algorithms. Combined with contextual association probability and contextual universal importance, a contextual preference prediction model was constructed to adjust the initial score of the traditional recommendation combined with item category preference to generate the final result. The example of the catering industry shows that the proposed algorithm is more effective than the improved algorithm

    08421 Abstracts Collection -- Uncertainty Management in Information Systems

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    From October 12 to 17, 2008 the Dagstuhl Seminar 08421 \u27`Uncertainty Management in Information Systems \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. The abstracts of the plenary and session talks given during the seminar as well as those of the shown demos are put together in this paper

    Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization

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    Data drives all aspects of our society, from everyday life, to business, to medicine, and science. It is well-known that query personalization can be an effective technique in dealing with the data scalability challenge, primarily from the human point of view. In order to personalize their query results, users need to express their preferences in an effective manner. There are two types of preferences: qualitative and quantitative. Each preference type has advantages and disadvantages with respect to expressiveness. The most important disadvantage of the quantitative model is that it cannot support all types of preferences while the qualitative model can only create a partial order over the data, which makes it impossible to rank all the results. The hypothesis of this dissertation is that it is possible to overcome the disadvantages of each preference type by combining both of them, in a single model, using the notion of intensity. This dissertation presents such a hybrid model and a practical system that has the ability to convert the intensity values of qualitative preferences into intensity values of quantitative preferences, without losing the qualitative information. The intensity values allow to create a total order over the tuples in the database that match a user’s preferences as well as to significantly increase the coverage of preferences. Hence, the proposed model eliminates the disadvantages of the existing two types of preferences. This dissertation formalizes the hybrid model using a preference graph and proposes an algorithm for efficient preference combination, which is evaluated in an experimental prototype. The experiments show that: (1) intensity plays a crucial role in determining the order of selecting and applying the preferences, and simply ordering the preferences based on the intensity value is not necessarily sufficient; (2) the model can achieve three orders of magnitude increase in coverage compared to other alternatives; (3) the solution proposed outperforms other Top-k algorithms by being able to use both qualitative and quantitative preferences at the same time, and (4) the algorithm proposed is efficient in terms of time complexity, returning tuples ordered by the intensity value in a matter of seconds

    GEMRec: A graph-based emotion-aware music recommendation approach

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    © Springer International Publishing AG 2016. Music recommendation has gained substantial attention in recent times. As one of the most important context features,user emotion has great potential to improve recommendations,but this has not yet been sufficiently explored due to the difficulty of emotion acquisition and incorporation. This paper proposes a graph-based emotion-aware music recommendation approach (GEMRec) by simultaneously taking a user’s music listening history and emotion into consideration. The proposed approach models the relations between user,music,and emotion as a three-element tuple (user,music,emotion),upon which an Emotion Aware Graph (EAG) is built,and then a relevance propagation algorithm based on random walk is devised to rank the relevance of music items for recommendation. Evaluation experiments are conducted based on a real dataset collected from a Chinese microblog service in comparison to baselines. The results show that the emotional context from a user’s microblogs contributes to improving the performance of music recommendation in terms of hitrate,precision,recall,and F1 score
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