Interactive systems have taken over the web and mobile space with increasing participation from users. Applications across every marketing domain can now be accessed through mobile or web where users can directly perform certain actions and reach a desired outcome. Actions of user on a system, though, can be representative of a certain intent. Ability to learn this intent through user's actions can help draw certain insight into the behavior of users on a system. In this paper, we present models to optimize interactive systems by learning and analyzing user intent through their actions on the system. We present a four phased model that uses time-series of interaction actions sequentially using a Long Short-Term Memory (LSTM) based sequence learning system that helps build a model for intent recognition. Our system then provides an objective specific maximization followed by analysis and contrasting methods in order to identify spaces of improvement in the interaction system. We discuss deployment scenarios for such a system and present results from evaluation on an online marketplace using user clickstream data.
@cite_15 performed a study on browser usage by using click-streams which are similar to our action sequences. This study made use of the similar sequences across different websites in order to understand parameters for the browser. We perform evaluation on the application under concern and treat the web browser as an independent platform. @cite_3 used linear models for predicting conversion likeliness among users with the help of usage features derived over time. Recently, @cite_12 presented analysis on courseware clickstream data in order to improve systems. @cite_1 use eye tracking methods for identifying intent of shopping. They study the dependence on eye actions directed towards system's outcome. Another common approach of defining user behavior on e-commerce websites is by building neural models as defined in @cite_6 and @cite_2 .
@cite_1 @cite_3 @cite_6 @cite_2 @cite_15 @cite_12
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Implicit intention is the intention that is not expressed externally but having in one's mind. Implicit intention is difficult to be recognized, but it can be significant information if it is recognized with some measures. When people buy something, they also have implicit intention in their mind, whether I buy this or not. We proposed an experimental paradigm to recognize shopper's implicit intention, and the result of experiment was analyzed in this paper. On the experiment, subjects were instructed to select items to buy from the candidates, and eye-tracking and speech data were recorded during the selection. On data analysis, measures discriminating the existence of implicit shopping intention were selected and compared. From the result, fixation duration, fixation count, multiplication of first fixation duration, and visit count showed different tendency between two cases: when people have intention to buy it and when people do not have. By using this standards, implicit shopping intention of people can be recognized. Online ecommerce has been booming for a decade. For instance, as the largest online C2C marketplace (eBay), millions of new items are listed daily. Due to the overwhelming number of items, the process of finding the right items to buy is sometimes daunting. In order to address this problem, this paper describes the idea of predicting the probability that a newly listed item will be sold successfully. And adjust the item exposure chances proportional according to their conversion possibility. Hence, by ranking higher items that users are likely to buy, the chance that users make the purchases could be increased as well as their user satisfaction. For catalog products that have been listed repeatedly, this probability can be measured empirically. However, on C2C sites like eBay, lots of items are not product-based. They are unique, and from different sellers. Therefore, in order to predict whether a new listing will be sold, we collect a large scale item set as the training data, and a set of features were used to model the average buyer shopping decision on C2C sites. Experimental results verified our system's feasibility and effectiveness. Understanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented as a sequence of observable and hidden events. The PGM framework provides a mathematically solid way to reason about a set of events given some information about other events. But the structure of the dependencies between the events has to be set manually. Different click models use different hand-crafted sets of dependencies. We propose an alternative based on the idea of distributed representations: to represent the user's information need and the information available to the user with a vector state. The components of the vector state are learned to represent concepts that are useful for modeling user behavior. And user behavior is modeled as a sequence of vector states associated with a query session: the vector state is initialized with a query, and then iteratively updated based on information about interactions with the search engine results. This approach allows us to directly understand user browsing behavior from click-through data, i.e., without the need for a predefined set of rules as is customary for PGM-based click models. We illustrate our approach using a set of neural click models. Our experimental results show that the neural click model that uses the same training data as traditional PGM-based click models, has better performance on the click prediction task (i.e., predicting user click on search engine results) and the relevance prediction task (i.e., ranking documents by their relevance to a query). An analysis of the best performing neural click model shows that it learns similar concepts to those used in traditional click models, and that it also learns other concepts that cannot be designed manually. In our study, we investigate the effectiveness of different models to the purchasing behaviour at YOOCHOOSE website. This paper provide a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and show that the result is comparable to using more massive feature engineering that requires session summarizing. Our proposed method requires much lesser feature engineering and more natural modeling of the click events directly in a typical purchasing session in e-commerce. This paper presents results of an extensive long-term click-stream study of Web browser usage. Focusing on character and challenges of page revisitation, previous findings from seven to thirteen years ago are updated. The term page re-visit had to be differentiated, since the recurrence rate--the key measure for the share of page revisits--turns out to strongly depend on interpretation. We identify different types of revisitation that allow assessing the quality of current user support and developing concepts for new tools. Individual navigation strategies differ dramatically and are strongly influenced by personal habits and type of site visited. Based on user action logs and interviews, we distinguished short-term revisits (backtrack or undo) from medium-term (re-utilize or observe) and long-term revisits (rediscover). We analyze current problems and provide suggestions for improving support for different revisitation types. MOOCs are popular for online education because of their convenience and excellent educational resources. However, online education for software engineering on MOOCs faces many challenges: (1) Software Engineering has wide coverage, but the teaching time is limited. So it is difficult to make in-depth education; (2) Because teachers and students are not face-to-face, it is difficult for teachers to know if students understand the lectures or not, and how to improve the online education for software engineering. In this paper, we propose an approach to using Coursera clickstream data to improve online education for software engineering. The key to the approach is to use the clickstream data produced by students while they are studying, for further analysis to find out some useful information, such as important content and difficult content of the course. Then, the teachers use the analysis result to know students' learning status and improve the teaching quality. Also, above all the results we have analyzed, we put forward several ways to improve the course Software Engineering.