Personality-based Recommender Systems Research Articles

Research Trend in Personality-based Recommender System

Research Trend in Personality-based Recommender System

The Performance of Personality-based Recommender System for Fashion with Demographic Data-based Personality Prediction

Currently, the common method to predict personality implicitly (Implicit Personality Elicitation) is Personality Elicitation from Text (PET). PET predicts personality implicitly based on statuses written on social media. The weakness of this method when applied to a recommender system is the requirement to have minimal one social media account. A user without such qualification cannot use such system. To overcome this shortcoming, a new method to predict personality implicitly based on demographic data is proposed. This proposal is based on findings by previous researchers stating that there is a correlation between demographic data and personality trait. To predict personality based on demographic data, a personality model (rule) is needed. This model correlates demographic data and personality. To apply this model to a recommender system, another model is needed, that is preference model which connects personality and preference. These two models are then applied to a personality-based recommender system for fashion. From performance evaluation, the precision of and user satisfaction to the recommendation is 60.19% and 87.50%, respectively. When compared to precision and user satisfaction of PET-based recommender system (which are 82% and 79%, respectively), the precision of demographic data-based recommender system is lower whereas the satisfaction is higher.

Cross-domain recommendation with user personality

Recommender systems have long played indispensable roles in e-commerce by helping users find their preferred products efficiently and accurately. In recent years, some pioneering works have shown that users’ intrinsic characteristics, such as personality traits, can help improve the performance of recommender systems. However, they generally do not consider how to obtain users’ personality traits effectively without burdensome surveys and, therefore, cannot be used in new application domains without adequate personality information. Therefore, we propose a novel framework for building cross-domain personality-based recommender systems, especially for personality-scarce target domains. Specifically, we define the cross-domain personality trait classification problem and solve it in a semisupervised manner by leveraging the predictive text embedding method as the method for transfer learning from the source to the target domain. We then design a personality-boosted probabilistic matrix factorization method for personality-based recommendations. Extensive experiments conducted on five real-world datasets demonstrate that users’ personality traits can be recognized more precisely with cross-domain transfer learning, and recommendation performance is improved accordingly.

TWIN: Personality-based Intelligent Recommender System

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 applie s 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.

PERSONALITYML: A MARKUP LANGUAGE TO STANDARDIZE THE USER PERSONALITY IN RECOMMENDER SYSTEMS

In recent years the study of how human psychological aspects may improve the decision-making process in computers has became a new trend. This subject has attracted the attention from both academy and industry in areas such as human-computer interaction, computer in education, recommender systems and social matching systems, among others. However, one of the biggest problems faced by them is how effectively to use, model and implement those psychological aspects in computers. This paper comes to fill partly this gap by proposing a markup language to standardize the representation of personality. The PersonalityML proposes a set of recommender inputs to be used as starting data to classical cold-start problem in recommender systems, as well as, in personality-based recommender systems and others personality-based web applications.