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Improving sparsity and new user problems in collaborative filtering by clustering the personality factors

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Abstract

In collaborative filtering recommender systems, items recommended to an active user are selected based on the interests of users similar to him/her. Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former refers to the insufficiency of data about users’ preferences and the latter addresses the lack of enough information about the new-coming user. Clustering users is an effective way to improve the performance of collaborative filtering systems in facing the aforementioned problems. In previous studies, users were clustered based on characteristics such as ratings given by them as well as their age, gender, occupation, and geographical location. On the other hand, studies show that there is a significant relationship between users’ personality traits and their interests. To alleviate the sparsity and new user problems, this paper presents a new collaborative filtering system in which users are clustered based on their ‘personality traits’. In the proposed method, the personality of each user is described according to the big-5 personality model and users with similar personality are placed in the same cluster using K-means algorithm. The unknown ratings of the sparse user-item matrix are then estimated based on the clustered users, and recommendations are found for a new user according to a user-based approach which relays on the interests of the users with similar personality to him/her. In addition, for an existing user in the system, recommendations are offered in an item-based approach in which the similarity of items is estimated based on the ratings of users similar to him/her in personality. The proposed method is compared to some former collaborative filtering systems. The results demonstrate that in facing the data sparsity and new user problems, this method reduces the mean absolute error and improves the precision of the recommendations.

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Acknowledgements

The authors would like to thank Dr. Mohammad Ali Nematbakhsh for his support and assistance to improve the manuscript.

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  1. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Zahra Yusefi Hafshejani, Marjan Kaedi & Afsaneh Fatemi

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  1. Zahra Yusefi Hafshejani
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Correspondence to Marjan Kaedi.

Appendix

Appendix

The NEO-FFI questionnaire comprises 60 items, 12 items to assess each of the five personality factors (six items are positively worded and six items are negatively worded for each personality factor). For each item, the respondent should describe how accurately that item describes him/her. The respondent selects the response based on the five-point Likert scale (ranging from strongly disagree to strongly agree). Then, the following scores are assigned to the answers of positively worded items:

Strongly disagree (1), Disagree (2), Neither agree nor disagree (3), Agree (4), Strongly agree (5) and the following scores are used for the negatively worded items:

Strongly disagree (5), Disagree (4), Neither agree nor disagree (3), Agree (2), Strongly agree (1).

Finally, the personality factors are assessed by summing up the scores. The NEO-FFI is a copyrighted questionnaire. Because of copyright compliance policy, only few sample statements are listed below:

Personality factor

Sample statement

Positive/negative

Neuroticism

I worry about things

Positive

Extraversion

I think a lot before I speak or act

Negative

Conscientiousness

I get chores done right away

Positive

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Yusefi Hafshejani, Z., Kaedi, M. & Fatemi, A. Improving sparsity and new user problems in collaborative filtering by clustering the personality factors. Electron Commer Res 18, 813–836 (2018). https://doi.org/10.1007/s10660-018-9287-x

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