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Personality-based Intelligent Recommender System, Journal of Intelligent and Fuzzy Systems

Abstract: this paper presents the “Tell me What I Need” (TWIN) Personality-based Intelligent Recommender System, the goal of which is to recommend items chosen by like-minded (or “twin”) people with similar personality types which we estimate from their writings. In order to produce recommendations it applies the results achieved in the personality from the text recognition research field to Personality-based Recommender System user profile modelling. In this way it creates a bridge between the efforts of automatic personality score estimation from plain text and the field of Intelligent Recommender Systems. The paper describes the TWIN system architecture, and results of the experimentation with the system in the online travelling domain in order to investigate the possibility of providing valuable recommendations of hotels of the TripAdvisor website for “like-minded people”. The results compare favourably with related experiments, although they demonstrate the complexity of this challenging task.


Abstract: the information overload experienced by people 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 recommender system, the aim of which is to select for the user reviews which have been written by like-minded individuals. We focus in particular 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 between 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.

ResourcesDatasets and other resources available for download.

TripAdvisor datasets2015

  • Tripadvisor dataset: Updated version of TripAdvisor Datasets released in 2012-2013.- detailed description of users' profiles- samples of 5 or more text reviews (for each user)- textual content of 1 article (available only for some users)Dataset description



  • Dataset 1: Contains 16637 reviews with personality scores and information about the author.
  • Dataset 2: Contains 15922 reviews sorted by the author id.

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TWIN is a great name for a personal recommender system.

This research demonstrates how hard this problem is. 

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