Title: Item-Based Collaborative Filtering Using the Big Five
Personality Traits
Abstract:
Collaborative filtering is a popular technique that makes high
quality recommendations. It is also able to recommend items from
multiple domains (e.g. books vs. movies). However, we found that it
makes more accurate recommendation within one domain than across
domains. The proposed system uses the personality of users to
generate the recommendations. It develops a profile for each item
that reflects the personality of users who like it. Item profiles
are used to give item-based collaborative filtering recommendations.
In the experiments, it is presented that the pure version of the
proposed system permits high accuracy. Moreover, the prediction
accuracy becomes greater when the system makes cross-domain
recommendations than when it works in one domain.