---
abstract: |
    Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the *individual evolution* of a user’s [*experience*]{} level and how this is expressed in the user’s [*writing*]{} in a review community.

    In this paper, we model the [*joint evolution*]{} of [*user experience*]{}, interest in specific [*item facets*]{}, [*writing style*]{}, and [*rating behavior*]{}. This way we can generate individual recommendations that take into account the user’s maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user’s experience and interests in a [*latent model*]{} learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time — with solely user reviews and ratings as observables over [*time*]{}. The facets of a user’s interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.
author:
- 
bibliography:
- 'kdd14.bib'
title: Item Recommendation with Evolving User Preferences and Experience
---


