Predicting Purchase Proneness of Anonymous User in Mobile Commerce

In recent years, mobile commerce is developing rapidly because of the popularity of mobile devices. However, for the difficulty of the mobile device input, the users of the e-commerce websites usually don’t log on the website when they are browsing, which resulting in a situation that a large number of website visitors are anonymous users. In order to increase sales revenue and expand market share, an effective prediction of anonymous users’ purchases proneness is very helpful in providing targeted marketing strategy for website to induce anonymous users to purchase. In the past, customer segmentation was mainly analyzed and modeled by customers’ historical data. But the history data of anonymous users can’t be obtained on mobile commerce sites. This method is difficult to put into management practice. In order to solve this problem, this paper proposes a method based on random forest of using user clickstream data to forecast purchase proneness in real time. This method includes two stages: the model training part and the user purchasing proneness prediction part. In the model training part, a classifier based on random forest algorithm is trained. In the users\u27 predicting part, the classifier is used to predict the user\u27s purchase proneness in real time. The method proposed can be effectively applied in the real-time prediction of anonymous users\u27 purchasing proneness, and the results of prediction will help enterprises implement the marketing measures in real time

Characteristics Description of Potential User Segments on the E-Commerce Website oriented to Precision Marketing

In the increasingly competitive environment between e-commerce companies, for more accurate implementation of marketing strategies, e-commerce websites often choose to subdivide the consumer market of the enterprise to identify site users’ characteristics to find their needs. In this paper, we subdivide consumer market from the four dimensions of behavior, geography, demography and psychology and propose a model to describe the characteristics of potential user market segments. Based on the web log data and user transaction data, a classification algorithm is used to analyze user behavior data in Web log to find the potential user segments and the user\u27s descriptive characteristics in user transaction data are clustered to obtain the distribution of consumer characteristics under various product categories, then we use the product categories in e-commerce website as an intermediary to give every single potential user in potential user market segments the descriptive characteristics, which can provide data support for the realization of precision marketing. The proposed model is applied to the actual data of a certain insurance e-commerce platform, and based on the results, we gain some implications for marketing of the e-commerce website

Mobile content personalisation using intelligent user profile approach

As there are several limitations using mobile internet, mobile content personalisation seems to be an alternative to enhance the experience of using mobile internet. In this paper, we propose the mobile content personalisation framework to facilitate collaboration between the client and the server. This paper investigates clustering and classification techniques using K-means and Artificial Neural Networks (ANN) to predict user's desired content and WAP pages based on device's listed-oriented menu approach. We make use of the user profile and user's information ranking matrix to make prediction of the desired information for the user. Experimental results show that it can generate promising prediction. The results show that it works best when used for predicting 1 matched menu item on the screen

Personalized Knowledge Service Based on Smart Cell-Phone Usage: A Conceptual Framework

Smart cell-phones include many advanced applications and services, which allow their users to achieve various useful goals. However, many users face difficulties when upgrading their cell-phones devices to more advanced ones, partially because their applications include more complex patterns of use for achieving the users’ goals. We present a conceptual framework that aims to help overcoming usage barrier by providing smart cell-phones’ users a personalized knowledge service. The framework is based on the utilization of task models and on the tracking and analyzing the usage of the applications included in the smart cell-phone that enable to construct users’ stereotypes and suggest personalized help according to their usage patterns. It is assumed that the system monitors the usage patterns of the user, thus enabling dynamic update of his/her belonging to a stereotype. The user can override the suggestions and navigate independently in order to find the required knowledge

Inferring Social-Demographics of Travellers based on Smart Card Data

[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promisingZhang, Y.; Cheng, T. (2018). Inferring Social-Demographics of Travellers based on Smart Card Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2018.2018.8310OCS556

Exploiting behavioral biometrics for user security enhancements

As online business has been very popular in the past decade, the tasks of providing user authentication and verification have become more important than before to protect user sensitive information from malicious hands. The most common approach to user authentication and verification is the use of password. However, the dilemma users facing in traditional passwords becomes more and more evident: users tend to choose easy-to-remember passwords, which are often weak passwords that are easy to crack. Meanwhile, behavioral biometrics have promising potentials in meeting both security and usability demands, since they authenticate users by who you are , instead of what you have . In this dissertation, we first develop two such user verification applications based on behavioral biometrics: the first one is via mouse movements, and the second via tapping behaviors on smartphones; then we focus on modeling user web browsing behaviors by Fitts\u27 Law.;Specifically, we develop a user verification system by exploiting the uniqueness of people\u27s mouse movements. The key feature of our system lies in using much more fine-grained (point-by-point) angle-based metrics of mouse movements for user verification. These new metrics are relatively unique from person to person and independent of the computing platform. We conduct a series of experiments to show that the proposed system can verify a user in an accurate and timely manner, and induced system overhead is minor. Similar to mouse movements, the tapping behaviors of smartphone users on touchscreen also vary from person to person. We propose a non-intrusive user verification mechanism to substantiate whether an authenticating user is the true owner of the smartphone or an impostor who happens to know the passcode. The effectiveness of the proposed approach is validated through real experiments. to further understand user pointing behaviors, we attempt to stress-test Fitts\u27 law in the wild , namely, under natural web browsing environments, instead of restricted laboratory settings in previous studies. Our analysis shows that, while the averaged pointing times follow Fitts\u27 law very well, there is considerable deviations from Fitts\u27 law. We observe that, in natural browsing, a fast movement has a different error model from the other two movements. Therefore, a complete profiling on user pointing performance should be done in more details, for example, constructing different error models for slow and fast movements. as future works, we plan to exploit multiple-finger tappings for smartphone user verification, and evaluate user privacy issues in Amazon wish list

Investigation of behavior and perception of digital library users: A cognitive style perspective

Cognitive style is an influential factor in users’ information seeking. The study presented in this paper examines how users’ cognitive styles affect their behavior and perception in digital libraries. Fifty participants took part in this study. Two dimensions of cognitive styles were considered: (a) Field Dependence/Independence; (2) Verbalizer/Imager. The results showed that Intermediate users and Verbalizers have not only more positive perception, but they also complete the tasks in effective ways. Implications for the design of personalized digital libraries are also discussed