Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3‐month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)‐based Tencent finance‐related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods‐based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen‐300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short‐term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

To fulfill users' search needs, the search engine must have good performance, easy-to-use functionalities, and good search result quality. Search quality evaluation becomes challenging when users' satisfaction may not be able to judge by a single search and even within a single search judgments from various sources are not consistent. In this talk, I will discuss how user's satisfaction is decomposed into different components in general, and how we measure them with various means - human judgment, automatic computation with query log, and outsourcing, and their pros and cons with operational implications. For an outlook, I will postulate potential evaluation approaches for a better user's satisfaction.

This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor, user opinions and social influence in a unified probabilistic model with two stages learning processes. In the first stage, topic factor and user influence are integrated to generate users' influential relationship based on different topics; in the second stage, users' historical messages and social interaction records are leveraged by TOIM to construct their historical opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. We evaluate our TOIM on a large-scaled dataset from Tencent Weibo, one of the largest microbloggings website in China. The experimental results show that TOIM can better predict users' opinion than other baseline methods.

In an endeavour to speak and prevail over some of the open problems that obstruct efficient networked media, this workshop will fetch together folks from a number of research communities, including but not limited to Multimedia Distribution and Access, Social Network Analysis, Multimedia Content Analysis, Behavioral Analysis, User Modelling Adaptation and Personalization. It is our credence that a synergetic approach involving the above mentioned research areas can surpass their individual potentials, leading to improved networked media access. The main objective of this workshop is to provide a forum to disseminate work that explicitly exploit the synergy between multimedia content analysis, behavioral modelling, personalisation, and next generation networking and community aspects of social networks. This synergetic methodology could produce high quality of experience for personalized multimedia access in networking environment.

Otolaryngology–Head and Neck SurgeryVolume 143, Issue S2 p. P143-P143 Poster Presentation Clinical Care Pathway: Improving Free Flap Patient Outcomes Brian Harmych MD, Brian Harmych MD presenterSearch for more papers by this authorGordon Sun MD, Gordon Sun MDSearch for more papers by this authorMeredith Tabangin MPH, Meredith Tabangin MPHSearch for more papers by this authorJareen Meinzen-Derr PhD, Jareen Meinzen-Derr PhDSearch for more papers by this authorYash Patil MD, Yash Patil MDSearch for more papers by this author Brian Harmych MD, Brian Harmych MD presenterSearch for more papers by this authorGordon Sun MD, Gordon Sun MDSearch for more papers by this authorMeredith Tabangin MPH, Meredith Tabangin MPHSearch for more papers by this authorJareen Meinzen-Derr PhD, Jareen Meinzen-Derr PhDSearch for more papers by this authorYash Patil MD, Yash Patil MDSearch for more papers by this author First published: 20 June 2017 https://doi.org/10.1016/j.otohns.2010.06.749Read the full textAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume143, IssueS2August 2010Pages P143-P143 RelatedInformation

It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this paper, we focus on extracting relevance information from one source of user interactions, i.e., user click data, which records the sequence of documents being clicked and not clicked in the result set during a user search session. We formulate the problem as a global ranking problem, emphasizing the importance of the sequential nature of user clicks, with the goal to predict the relevance labels of all the documents in a search session. This is distinct from conventional learning to rank methods that usually design a ranking model defined on a single document; in contrast, in our model the relational information among the documents as manifested by an aggregation of user clicks is exploited to rank all the documents jointly. In particular, we adapt several sequential supervised learning algorithms, including the conditional random field (CRF), the sliding window method and the recurrent sliding window method, to the global ranking problem. Experiments on the click data collected from a commercial search engine demonstrate that our methods can outperform the baseline models for search results re-ranking.

Ranking functions determine the relevance of search results of search engines, and learning ranking functions has become an active research area at the interface between Web search, information retrieval and machine learning. Generally, the training data for learning to rank come in two different forms: (1) absolute relevance judgments assessing the degree of relevance of a document with respect to a query. This type of judgments is also called labeled data and are usually obtained through human editorial efforts; and (2) relative relevance judgments indicating that a document is more relevant than another with respect to a query. This type of judgments is also called preference data and can usually be extracted from the abundantly available user click-through data recording users' interactions with the search results. Most existing learning to rank methods ignore the query boundaries, treating the labeled data or preference data equally across queries. In this paper, we propose a minimum effort optimization method that takes into account the entire training data within a query at each iteration. We tackle this optimization problem using functional iterative methods where the update at each iteration is computed by solving an isotonic regression problem. This more global approach results in faster convergency and signficantly improved performance of the learned ranking functions over existing state-of-the-art methods. We demonstrate the effectiveness of the proposed method using data sets obtained from a commercial search engine as well as publicly available data.

This paper describes a Chinese part-ofspeech tagging system based on the maximum entropy model. It presents a novel two-stage approach to using the part-ofspeech tags of the words on both sides of the current word in Chinese part-of-speech tagging. The system is evaluated on four corpora at the Fourth SIGHAN Bakeoff in the close track of the Chinese part-ofspeech tagging task.

In Web search ranking, the expected results for some queries could vary greatly depending upon location of the user. We name such queries regional sensitive queries. Identifying regional sensitivity of queries is important to meet users' needs. The objective of this work is to identify whether a user expects only regional results for a query. We present three novel features generated from search logs and build a meta query classifier to identify regional sensitive query. Experimental results show that the proposed method achieves high accuracy in identifying regional sensitive queries.

Learning to rank has become a popular method for web search ranking. Traditionally, expert-judged examples are the major training resource for machine learned web ranking, which is expensive to get for training a satisfactory ranking function. The demands for generating specific web search ranking functions tailored for different domains, such as ranking functions for different regions, have aggravated this problem. Recently, a few methods have been proposed to extract training examples from user click through log. Due to the low cost of getting user preference data, it is attractive to combine these examples in training ranking functions. However, because of the different natures of the two types of data, they may have different influences on ranking function. Therefore, it is challenging to develop methods for effectively combining them in training ranking functions. In this paper, we address the problem of adapting an existing ranking function to user preference data, and develop a framework for conveniently tuning the contribution of the user preference data in the tuned ranking function. Experimental results show that with our framework it is convenient to generate a batch of adapted ranking functions and to select functions with different tradeoffs between the base function and the user preference data.