User Modeling Using LSTM Networks

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

Student Abstract Track

The LSTM model presented is capable of describing a user of a particular website without human expert supervision. In other words, the model is able to automatically craft features which depict attitude, intention and the overall state of a user. This effect is achieved by projecting the complex history of the user (sequence data corresponding to his actions on the website) into fixed-size vectors of real numbers. The representation obtained may be used to enrich typical models used in e-commerce: click-through rate, conversion rate, recommender systems etc. The goal of this paper is to demonstrate a way of creating the mentioned projection, which we called user2vec, and present possible benefits of incorporating this solution to enhance conversion rate model. Thus enriched modelÕs superiority is due not only to its increased internal complexity but also to its capability of learning from wider data Ð it indirectly analyzes actions of all website users, rather than being limited to the users who clicked on an ad.

10.1609/aaai.v31i1.11068