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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Abstract The authors develop and estimate a model of online buying using clickstream data from a Web site that sells cars. The model predicts online buying by linking the purchase decision to what visitors do and to what they are exposed to while at the site. To overcome the challenges of predicting Internet buying, the authors decompose the purchase process into the completion of sequential nominal user tasks and account for heterogeneity across visitors at the county level. Using a sequence of binary probits, the authors model the visitor’s decision of whether to complete each task for the first time, given that the visitor has completed the previous tasks at least once. The results indicate that visitors’ browsing experiences and navigational behavior predict task completion for all decision levels. The results also indicate that the number of repeat visits per se is not diagnostic of buying propensity and that a site’s offering of sophisticated decision aids does not guarantee increased conversion rat... This empirical study investigates the contribution of different types of predictors to the purchasing behaviour at an online store. We use logit modelling to predict whether or not a purchase is made during the next visit to the website using both forward and backward variable-selection techniques, as well as Furnival and Wilson’s global score search algorithm to find the best subset of predictors. We contribute to the literature by using variables from four different categories in predicting online-purchasing behaviour: (1) general clickstream behaviour at the level of the visit, (2) more detailed clickstream information, (3) customer demographics, and (4) historical purchase behaviour. The results show that predictors from all four categories are retained in the final (best subset) solution indicating that clickstream behaviour is important when determining the tendency to buy. We clearly indicate the contribution in predictive power of variables that were never used before in online purchasing studies. Detailed clickstream variables are the most important ones in classifying customers according to their online purchase behaviour. In doing so, we are able to highlight the advantage of e-commerce retailers of being able to capture an elaborate list of customer information. Abstract Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor’s decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors’ propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with “within-site lock-in” or site “stickiness” and with learning that carries over repeat visits. In particular, repeat vis... When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.