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The information overload experienced by peo- ple who use online services and read user- generated content (e.g. product reviews and ratings) to make their decisions has led to the development of the so-called recommender systems. We address the problem of the large increase in the user-generated reviews, which are added to each day and consequently make it difficult for the user to obtain a clear picture of the quality of the facility in which they are interested.
In this paper, we describe the TWIN (“Tell me What I Need”) personality-based recom- mender system, the aim of which is to select for the user reviews which have been written by like-minded individuals. We focus in par- ticular on the task of User Profile construction. We apply the system in the travelling domain,
to suggest hotels from the TripAdvisor site by filtering out reviews produced by people with similar, or like-minded views, to those of the user. In order to establish the similarity be- tween people we construct a user profile by modelling the user’s personality (according to the Big Five model) based on linguistic cues collected from the user-generated text.
Roshchina A., Cardiff J., Rosso P., User Profile Construction in the TWIN Personality-based Recommender System, Workshop on Sentiment Analysis where AI meets Psychology SAAIP, 5th International Joint Conference on Natural Language Processing, IJCNLP, 2011