Continuous Experience-aware Language Model

author: Subhabrata Mukherjee, Max Planck Institute for Informatics, Max Planck Institute
published: Sept. 27, 2016,   recorded: August 2016,   views: 2010

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Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends. Recent work has shown that recommender systems benefit from explicit consideration of user experience. However, prior work assumes a fixed number of discrete experience levels, whereas in reality users gain experience and mature continuously over time.

This paper presents a new model that captures the continuous evolution of user experience, and the resulting language model in reviews and other posts. Our model is unsupervised and combines principles of Geometric Brownian Motion, Brownian Motion, and Latent Dirichlet Allocation to trace a smooth temporal progression of user experience and language model respectively. We develop practical algorithms for estimating the model parameters from data and for inference with our model (e.g., to recommend items). Extensive experiments with five real-world datasets show that our model not only fits data better than discrete-model baselines, but also out-performs state-of-the-art methods for predicting item ratings.

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Reviews and comments:

Comment1 Well Wisher, July 28, 2017 at 4:03 a.m.:

This is a highly demotivating talk because of the speaker's body language and expressions. He doesn't smile, doens't keep eye contact with the audience and seems to read scribbled notes. Overall he himself is not enthusiastic about his own work. These hides the technicalities or non-trivialities of the research work as he loses the audience on the first place.

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