Characterization Of User Behavior Research Articles

Embedding a Microblog Context in Ephemeral Queries for Document Retrieval

With the proliferation of information globally, the search engine had become an indispensable tool that helps the user to search for information in a simple, easy and quick way. These search engines employ sophisticated document ranking algorithms based on query context, link structure and user behavior characterization. However, all these features keep changing in the real scenario. Ideally, ranking algorithms must be robust enough to time-sensitive queries. Microblog content is typically short-lived as it is often intended to provide quick updates or share brief information in a concise manner. The technique first determines if a query is currently in high demand, then it automatically appends a time-sensitive context to the query by mining those microblogs whose torrent matches with query-in-demand. The extracted contextual terms are further used in re-ranking the search results. The experimental results reveal the existence of a strong correlation between ephemeral search queries and microblog volumes. These volumes are analyzed to identify the temporal proximity of their torrents. It is observed that approximately 70% of search torrents occurred one day before or after blog torrents for lower threshold values. When the threshold is increased, the match ratio of torrent is raised to ∼90%. In addition, the performance of the proposed model is analyzed for different combining principles namely, aggregate relevance (AR) and disjunctive relevance (DR). It is found that the DR variant of the proposed model outperforms the AR variant of the proposed model in terms of relevance and interest scores. Further, the proposed model’s performance is compared with three categories of retrieval models: log-logistic model, sequential dependence model (SDM) and embedding based query expansion model (EQE1). The experimental results reveal the effectiveness of the proposed technique in terms of result relevancy and user satisfaction. There is a significant improvement of ∼25% in the result relevance score and ∼35% in the user satisfaction score compared to underlying retrieval models. The work can be expanded in many directions in the future as various researchers can combine these strategies to build a recommendation system, auto query reformulation system, Chatbot, and NLP professional toolkit.

Fuzzy-based User Behavior Characterization to Detect HTTP-GET Flood Attacks

Fuzzy-based User Behavior Characterization to Detect HTTP-GET Flood Attacks

Large-Scale User Behavior Characterization of Online Video Service in Cellular Network

Online video service has become prevalent in recent years. A better understanding of the user behavior of such service is crucial for allocating the network resources and adjusting the service design. While there are some measurement studies on the non-mobile video services in fixed network, the usage of video service in mobile network is yet to be explored. In this paper, we present a detailed analysis of the user behavior characteristics of a leading comprehensive online video service, namely Youku, in cellular network. This paper is based on a large-scale data set containing over 17 billion traffic traces, collected from a major cellular network in Northeastern China. We analyze the user behavior from three key aspects: data consumption, service usage, and mobility. We provide an insight into how the mobile video service is utilized by users (especially the heavy users), by measuring the user intensities and various representative behavior features in each analysis aspect, such as active time, replay rate, video category, access location, and residence time. We reveal the patterns of different user behaviors, and discuss the implications for practical application. The findings of this paper can provide direct help for network operators and service providers to improve the network performance and user experience.

Aggregate Characterization of User Behavior in Twitter and Analysis of the Retweet Graph

Most previous analysis of Twitter user behavior has focused on individual information cascades and the social followers graph, in which the nodes for two users are connected if one follows the other. We instead study aggregate user behavior and the retweet graph with a focus on quantitative descriptions. We find that the lifetime tweet distribution is a type-II discrete Weibull stemming from a power law hazard function, that the tweet rate distribution, although asymptotically power law, exhibits a lognormal cutoff over finite sample intervals, and that the inter-tweet interval distribution is a power law with exponential cutoff. The retweet graph is small-world and scale-free, like the social graph, but less disassortative and has much stronger clustering. These differences are consistent with it better capturing the real-world social relationships of and trust between users than the social graph. Beyond just understanding and modeling human communication patterns and social networks, applications for alternative, decentralized microblogging systems---both predicting real-word performance and detecting spam---are discussed.

Role defining using behavior-based clustering in telecommunication network

Understanding the individual behavior has shown to be of paramount importance to the triumph of the telecommunication operators to retain customers, enhance their purchasing capacity, and predict the churn rate. Different behavior patterns can be observed for different groups of users. Hence, there is an interesting problem posted in telecommunication network that how to define the users’ role according to their behavior patterns. Traditionally, user behavior characterization methods generally based on their call detail record (CDR), which are user’s individual features, are not appropriate to identify the role in network. In this paper, we develop a new methodology for identifying users’ role based on their behaviors in telecommunication network using the social features instead of their individual features. Experiments have tested on synthetic data and large real datasets, and reveal good results on both of them. Finally, the methodology is not only limited to call graphs but also apply to other networks for role defining.