Abstract
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.
Highlights
The first method to be used in a recommender system was content-based filtering which recommend items based on similarity between keywords on item description and on user’s profile [1][2]
A rating-based collaborative filtering has several weaknesses, one of which is cold start problem or the new user problem [5]. This issue occurs when a recommender system is unable to provide a new user with accurate recommendations, because a new user does not have a record of what items has been consumed and the rating
This paper presents the result of the work in creating personality model connecting demographic data and personality traits
Summary
The first method to be used in a recommender system was content-based filtering which recommend items based on similarity between keywords on item description and on user’s profile [1][2] As it turned out, a content-based filtering has several weaknesses, one of which is its inability to distinguish the quality of items. The inability of content-based method to differentiate between different item qualities is solved by collaborative filtering by asking users to rate all the consumed items. This rating data is used to calculate the rating of all new items [3], this method will select top N items with highest ratings and recommend these items along with the estimated ratings [4]. This issue occurs when a recommender system is unable to provide a new user with accurate recommendations, because a new user does not have a record of what items has been consumed and the rating (the user profile is still empty)
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