Generative AI (GenAI) offers new opportunities for customer support in online travel agencies, yet little is known about how its design influences user engagement, purchase behavior, and user experience. We report results from a randomized field experiment in online travel itinerary planning, comparing GenAI that expressed (A) positive enthusiasm, (B) neutral expression, and (C) no tone instructions (control). Users in group A wrote significantly longer prompts than those in groups B and C. At the same time, users in groups A and B were more likely to purchase subscriptions of the webservice. We further analyze linguistic cues across experimental groups to explore differences in user experience and explain subscription purchases and affiliate link clicks based on these cues. Our findings provide implications for the design of persuasive and engaging GenAI interfaces in consumer-facing contexts and contribute to understanding how linguistic framing shapes user behavior in AI-mediated decision support.

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.

An A.I.-powered online fashion marketplace, Shoptrue, is launching its website into beta today with plans for a public release early next year. The site blends artificial intelligence and personalized recommendations with taste-driven shopping, the company says, which helps give users a source for style inspiration as well as the ability to create and share outfit ideas with others. Rather than the typical algorithmic approach such as Amazon, which ranks items based on a strong sales history, Shoptrue is A.I.-driven and continually improves its product recommendations based on purchase behaviors and user engagement. That way, users can have more say on what items they see on their curated feeds. The site offers a "One Stop Personal Shop" for the user, which gives fashion suggestions based on their style preferences.

The E-commerce platform has become the principal battleground where people search, browse and pay for whatever they want. Critical as is to improve the online shopping experience for customers and merchants, how to find a proper approach for user intent prediction are paid great attention in both industry and academia. In this paper, we propose a novel user intent prediction model, TPG-DNN, to complete the challenging task, which is based on adaptive gated recurrent unit (GRU) loss function with multi-task learning. We creatively use the GRU structure and total probability formula as the loss function to model the users' whole online purchase process. Besides, the multi-task weight adjustment mechanism can make the final loss function dynamically adjust the importance between different tasks through data variance. According to the test result of experiments conducted on Taobao daily and promotion data sets, the proposed model performs much better than existing click through rate (CTR) models. At present, the proposed user intent prediction model has been widely used for the coupon allocation, advertisement and recommendation on Taobao platform, which greatly improve the user experience and shopping efficiency, and benefit the gross merchandise volume (GMV) promotion as well.

Fujitsu Laboratories Ltd. and Fujitsu Research and Development Center Co., Ltd. have innovated an AI technology for video-based behavioral analysis. Dubbed "Actlyzer", the tech can recognize a variety of subtle and complex human activities without relying on large amounts of training data. Deep learning technologies conventionally demand large amounts of video data for training systems to recognize individual behaviors, and video data must be collected from scratch in order to add each new behavior. This time-consuming process means that it can often take several months to introduce functional AI into the field. Taking advantage of the fact that human behaviors generally consist of a combination of basic movements and actions, (e.g.

Artificial intelligence is already an effective marketing tool in large-scale marketing channels, but recently it has made headway in mobile video advertising. A recent survey of 1,000 mobile users asked if after watching a mobile video ad whether viewers were inclined to buy a product. Nearly half of those surveyed said they never bought a product after watching a commercial video. Additionally, when asked why this was the case, 51 per cent stated they were not in the market for the product at that time of viewing and 29 per cent said the adverts were not relevant. These statistics for mobile video viewers indicated that advertising is not being delivered at the correct point in the purchase funnel or to users who were likely to change their minds, ultimately wasting marketer's budgets. "The challenge many brands face is getting ROI from their digital campaigns and achieving the marketing goals" comments Stephen Upstone, CEO LoopMe.

We propose a new topic model for tracking time-varying consumer purchase behavior, in which consumer interests and item trends change over time. The proposed model can adaptively track changes in interests and trends based on current purchase logs and previously estimated interests and trends. The online nature of the proposed method means we do not need to store past data for current inferences and so we can considerably reduce the computational cost and the memory requirement. We use real purchase logs to demonstrate the effectiveness of the proposed method in terms of the prediction accuracy of purchase behavior and the computational cost of the inference.