Modelling Visit Similarity Using Click-Stream Data: A Supervised Approach

Pai, Deepak; Sharang, Abhijit; Yadagiri, Meghanath Macha; Agrawal, Shradha

doi:10.1007/978-3-319-11749-2_11

Cited by 9 publications

(10 citation statements)

References 13 publications

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“…While the events could be directly fed into CrEOS, URLs would need to be pre-processed. This could be done by leveraging some of the earlier studies [3,13] that detail categorization of click URLs. Further, each event in the event stream can be augmented with other contextual features such as time of day, weekday/weekend, price of the product etc.…”

Section: Methods 21 Marketer's Workflowmentioning

confidence: 99%

“…We break down the event stream into multiple consumer sessions whenever there is an inactivity of thirty minutes between two events. The sessionization of clickstream data is a standard pre-processing technique in clickstream analysis [12,13]. Next, we remove all sessions with less than five click events.…”

Section: Methodsmentioning

confidence: 99%

“…[5] for a survey). While the earlier works [3,13] relied on extensive feature engineering from the user sessions to predict purchase intent, recently the trend has shifted to employ end to end deep learning techniques. These primarily involve learning a single variable [8,11,12,14] LSTM where the input is a consumer's event stream to predict the conversion.…”

Section: Introductionmentioning

confidence: 99%

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CrEOS: Identifying Critical Events in Online Sessions

Macha

Venkitachalam

Pai

2020

Companion Proceedings of the Web Conference 2020

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Analyzing a consumer's experience in an online session is a fundamental task of a marketer to enrich, personalize and enhance their future interactions with a firm. Click (web) or tap (mobile) event streams efficiently capture a consumer's experience in an online session. In this work, we propose CrEOS contributing to the extant literature in the deployment of deep learning techniques to model online consumer behaviour. CrEOS models consumer conversion behaviour using a multi-variable LSTM, enriching the consumer event stream with a series of contextual features. In addition to the predictions, CrEOS identifies events that either hamper or help a conversion at three levels-consumer, consumer segment and transitions in marketing funnel. We validate CrEOS on click-stream data of a large US based e-commerce firm, compare it with single variable LSTM and discuss the insights derived from the critical event identification considering a series of marketing tasks. CCS CONCEPTS • Applied computing → Online shopping; • Information systems → Web mining.

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Section: Methods 21 Marketer's Workflowmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CrEOS: Identifying Critical Events in Online Sessions

Macha

Venkitachalam

Pai

2020

Companion Proceedings of the Web Conference 2020

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“…Our approach is based on building features from the click-stream data, computing similarity score between visitor pairs and using kNN to classify visitors. The details of methodology for classifying visitors is described in [3], where we show that our methods outperform prior-art systems. The same methodology is used to identify visitors similar to any selected visitor, thus creating a visitor segment.…”

Section: Architecturementioning

confidence: 97%

Real-time visualization and targeting of online visitors

Pai¹,

George²

2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Identifying and targeting visitors on an e-commerce website with personalized content in real-time is extremely important to marketers. Although such targeting exists today, it is based on demographic attributes of the visitors. We show that dynamic visitor attributes extracted from their clickstream provide much better predictive capabilities of visitor intent. In this demonstration, we showcase an interactive real-time user interface for marketers to visualize and target visitor segments. Our dashboard not only provides the marketers understanding of their visitor click patterns, but also lets them target individual or group of visitors with offers and promotions.

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“…Other important descriptions of behaviors are click-stream [20] and web session [21] that reflect users' behavior patterns. However, it is still a blank towards the students' operation series.…”

Section: Related Workmentioning

confidence: 99%

A behavioral sequence analyzing framework for grouping students in an e-learning system

Xie

Zheng

Zhang

2016

Knowledge-Based Systems

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Grouping of students benefits the formation of virtual learning communities, and contributes to collaborative learning space and recommendation. However, the existed grouping criteria are mainly limited in the learning portfolios, profiles, and social attributes etc. In this paper, we aim to build a unified framework for grouping students based on the behavioral sequences and further predicting which group a newcomer will be. The sequences are represented as a series of behavioral trajectories. We discuss a shape descriptor to approximately express the geometrical information of trajectories, and then capture the structural, micro, and hybrid similarities. A weighted undirected graph, using the sequence as a node, the relation as an edge, and the similarity as the weight, is constructed, on which we perform an extended spectral clustering algorithm to find fair groups. In the phase of prediction, an indexing and retrieval scheme is proposed to assign a newcomer to the corresponding group. We conduct some preliminary experiments on a real dataset to test the availability of the framework and to determine the parameterized conditions for an optimal grouping. Additionally, we also experiment on the grouping prediction with a synthetic data generator. Our proposed method outperforms the counterparts and makes grouping more meaningful.

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Modelling Visit Similarity Using Click-Stream Data: A Supervised Approach

Cited by 9 publications

References 13 publications

CrEOS: Identifying Critical Events in Online Sessions

CrEOS: Identifying Critical Events in Online Sessions

Real-time visualization and targeting of online visitors

A behavioral sequence analyzing framework for grouping students in an e-learning system

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