Studies on User Intent Analysis and Mining

Predicting the goals of users can be extremely useful in e-commerce, online entertainment, information retrieval, and many other online services and applications. In this thesis, we study the task of user intent understanding, trying to bridge the gap between user expressions to online services and their goals behind it. As far as we know, most of the existing user intent studies are focusing on web search and social media domain. Studies on other areas are not enough. For example, as people more and more rely our daily life on cellphone, our information needs expressing to mobile devices and related services are increasing dramatically. Studies of user intent mining on mobile devices are not much. And the intentions of using mobile devices are different from the ones we use web search engine or social network. So we cannot directly apply the existing user intention to this area. Besides, user's intents are not stable but changing over time. And different interests will impact each other. Modeling such kind of dynamic user interests can help accurately understand and predict user's intent. But there're few existing works in this area. Moreover, user intent could be explicitly or implicitly expressed by users. The implicit intent expression is more close to human's natural language and also have great value to recognize and mine. To make further studies of these challenges, we first try to answer the question of “What is the user intent?” By referring amount of previous studies, we give our definition of user intent as “User intent is a task-specific, predefined or latent concept, topic or knowledge-base that is under an expression from a user who is trying to express his goal of information or service need.“ Then, we focus on the driving scenario when a user using cellphone and study the user intent in this domain. As far as we know, it is the first time of user intent analysis and categorization in this domain. And we also build a dataset of user input and related intent category and attributes by crowdsourcing and carefully handcraft. With the user intent taxonomy and dataset in hand, we conduct a user intent classification and user intent attribute recognition by supervised machine learning models. To classify the user intent for a user intent query, we use a convolutional neural network model to build a multi-class classifier. And then we use a sequential labeling method to recognize the intent attribute in the query. The experiment results show that our proposed method outperforms several baseline models in precision, recall, and F-score. In addition, we study the implicit user intent mining method through web search log data. By using a Restricted Boltzmann Machine, we make use of the correlation of query and click information to learn the latent intent behind a user web search. We propose a user intent prediction model on online discussion forum using Multivariate Hawkes Process. It dynamically models user intentions change and interact over time.The method models both of the internal and external factors of user's online forum response motivations, and also integrated the time decay fact of user's interests. We also present a data visualization method, using an enriched domain ontology to highlight the domain-specific words and entity relations within an article.Ph.D., Information Studies -- Drexel University, 201

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

The Impact of Perceived Interactivity and Vividness of Video Games on Customer Buying Behavior

About 60 percent of Americans (145 million people) play video games, and the age of 61 percent of all game players is 18 and over (IDSA, 200 1). As the competition to excel in the video game market increasingly becomes difficult for manufacturers, it is becoming more important for manufacturers and video game developers to understand what makes people play and buy games. The major challenge to the gaming industry is to figure out what features of games can catch the consumers\u27 attention. The purpose of this research was to examine what kinds of video games captivate consumers, determine whether more interactivity and vividness in games achieve more positive press, and evaluate how video games of the future should be developed. A survey of 228 game players in U.S.A. was conducted; research results were generated through the use of descriptive analysis, correlation analysis, and multiple regression analysis. The results of this study showed that a video game\u27s creativity, challenge, control, sensory gratification, socialization, audio effect, visual effect, and storytelling have positive relevance to engage consumers\u27 minds and stimulate their imagination to play or purchase video games. The results also showed that gender differences can influence the individual types of video games purchased. Three age groups (18 to 24,25 to 34, and 35 to 56) had different patterns of purchasing video games. The results showed that respondents\u27 buying behavior is significantly influenced by the characteristics of interactivity and vividness. This study contributed to developing the characteristics of video games by identifying to what extent consumers\u27 emotional responses and behaviors are directly affected by interactivity and vividness in gaming characteristics. The framework of this study can be used to analyze and evaluate customer buying behavior in various video games in the industry. To increase the video game marketplace, merging the features of interactivity and vividness may be a key to enhancing customers\u27 buying behavior and playing intentions

Combating Threats to the Quality of Information in Social Systems

Many large-scale social systems such as Web-based social networks, online social media sites and Web-scale crowdsourcing systems have been growing rapidly, enabling millions of human participants to generate, share and consume content on a massive scale. This reliance on users can lead to many positive effects, including large-scale growth in the size and content in the community, bottom-up discovery of “citizen-experts”, serendipitous discovery of new resources beyond the scope of the system designers, and new social-based information search and retrieval algorithms. But the relative openness and reliance on users coupled with the widespread interest and growth of these social systems carries risks and raises growing concerns over the quality of information in these systems. In this dissertation research, we focus on countering threats to the quality of information in self-managing social systems. Concretely, we identify three classes of threats to these systems: (i) content pollution by social spammers, (ii) coordinated campaigns for strategic manipulation, and (iii) threats to collective attention. To combat these threats, we propose three inter-related methods for detecting evidence of these threats, mitigating their impact, and improving the quality of information in social systems. We augment this three-fold defense with an exploration of their origins in “crowdturfing” – a sinister counterpart to the enormous positive opportunities of crowdsourcing. In particular, this dissertation research makes four unique contributions: • The first contribution of this dissertation research is a framework for detecting and filtering social spammers and content polluters in social systems. To detect and filter individual social spammers and content polluters, we propose and evaluate a novel social honeypot-based approach. • Second, we present a set of methods and algorithms for detecting coordinated campaigns in large-scale social systems. We propose and evaluate a content- driven framework for effectively linking free text posts with common “talking points” and extracting campaigns from large-scale social systems. • Third, we present a dual study of the robustness of social systems to collective attention threats through both a data-driven modeling approach and deploy- ment over a real system trace. We evaluate the effectiveness of countermeasures deployed based on the first moments of a bursting phenomenon in a real system. • Finally, we study the underlying ecosystem of crowdturfing for engaging in each of the three threat types. We present a framework for “pulling back the curtain” on crowdturfers to reveal their underlying ecosystem on both crowdsourcing sites and social media

Beyond subjective and objective in statistics

Decisions in statistical data analysis are often justified, criticized or avoided by using concepts of objectivity and subjectivity. We argue that the words 'objective' and 'subjective' in statistics discourse are used in a mostly unhelpful way, and we propose to replace each of them with broader collections of attributes, with objectivity replaced by transparency, consensus, impartiality and correspondence to observable reality, and subjectivity replaced by awareness of multiple perspectives and context dependence. Together with stability, these make up a collection of virtues that we think is helpful in discussions of statistical foundations and practice. The advantage of these reformulations is that the replacement terms do not oppose each other and that they give more specific guidance about what statistical science strives to achieve. Instead of debating over whether a given statistical method is subjective or objective (or normatively debating the relative merits of subjectivity and objectivity in statistical practice), we can recognize desirable attributes such as transparency and acknowledgement of multiple perspectives as complementary goals. We demonstrate the implications of our proposal with recent applied examples from pharmacology, election polling and socio-economic stratification. The aim of the paper is to push users and developers of statistical methods towards more effective use of diverse sources of information and more open acknowledgement of assumptions and goals

Network Structures, Concurrency, and Interpretability: Lessons from the Development of an AI Enabled Graph Database System

This thesis describes the development of the SmartGraph, an AI enabled graph database. The need for such a system has been independently recognized in the isolated fields of graph databases, graph computing, and computational graph deep learning systems, such as TensorFlow. Though prior works have investigated some relationships between these fields, we believe that the SmartGraph is the first system designed from conception to incorporate the most significant and useful characteristics of each. Examples include the ability to store graph structured data, run analytics natively on this data, and run gradient descent algorithms. It is the synergistic aspects of combining these fields that provide the most novel results presented in this dissertation. Key among them is how the notion of “graph querying” as used in graph databases can be used to solve a problem that has plagued deep learning systems since their inception; rather than attempting to embed graph structured datasets into restrictive vector spaces, we instead allow the deep learning functionality of the system to natively perform graph querying in memory during optimization as a way of interpreting (and learning) the graph. This results in a concept of natural and interpretable processing of graph structured data. Graph computing systems have traditionally used distributed computing across multiple compute nodes (e.g. separate machines connected via Ethernet or internet) to deal with large-scale datasets whilst working sequentially on problems over entire datasets. In this dissertation, we outline a distributed graph computing methodology that facilitates all the above capabilities (even in an environment consisting of a single physical machine) while allowing for a workflow more typical of a graph database than a graph computing system; massive concurrent access allowing for arbitrarily asynchronous execution of queries and analytics across the entire system. Further, we demonstrate how this methodology is key to the artificial intelligence capabilities of the system

Response Retrieval in Information-seeking Conversations

The increasing popularity of mobile Internet has led to several crucial changes in the way that people use search engines compared with traditional Web search on desktops. On one hand, there is limited output bandwidth with the small screen sizes of most mobile devices. Mobile Internet users prefer direct answers on the search engine result page (SERP). On the other hand, voice-based / text-based conversational interfaces are becoming increasing popular as shown in the wide adoption of intelligent assistant services and devices such as Amazon Echo, Microsoft Cortana and Google Assistant around the world. These important changes have triggered several new challenges that search engines have had to adapt to in order to better satisfy the information needs of mobile Internet users. In this dissertation, we investigate several aspects of single-turn answer retrieval and multi-turn information-seeking conversations to handle the new challenges of search on the mobile Internet. We start from the research on single-turn answer retrieval and analyze the weaknesses of existing deep learning architectures for answer ranking. Then we propose an attention based neural matching model with a value-shared weighting scheme and attention mechanism to improve existing deep neural answer ranking models. Our proposed model achieves state-of-the-art performance for answer sentence retrieval compared with both feature engineering based methods and other neural models. Then we move on to study response retrieval in multi-turn information-seeking conversations beyond single-turn interactions. Much research on response selection in conversation systems is modeling the matching patterns between user input message (either with context or not) and response candidates, which ignores external knowledge beyond the dialog utterances. We propose a learning framework on top of deep neural matching networks that leverages external knowledge with pseudo-relevance feedback and QA correspondence knowledge distillation for response retrieval. We also study how to integrate user intent modeling into neural ranking models to improve response retrieval performance. Finally, hybrid models of response retrieval and generation are investigated in order to combine the merits of these two different paradigms of conversation models. Our goal is to develop effective learning models for answer retrieval and information-seeking conversations, in order to improve the effectiveness and user experience when accessing information with a touch screen interface or a conversational interface, as commonly adopted by millions of mobile Internet devices