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User Personality and User Satisfaction with Recommender Systems

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Abstract

In this study, we show that individual users’ preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual preferences for recommendation diversity, popularity and serendipity by measuring their personality traits. We conducted an online experiment with over 1,800 users for six months on a live recommendation system. In this experiment, we asked users to evaluate a list of movie recommendations with different levels of diversity, popularity, and serendipity. Then, we assessed users’ personality traits using the Ten-item Personality Inventory (TIPI). We found that ratings-based recommender systems may often fail to deliver preferred levels of diversity, popularity, and serendipity for their users (e.g. users with high-serendipity preferences). We also found that users with different personalities have different preferences for these three recommendation properties. Our work suggests that we can improve user satisfaction when we integrate users’ personality traits into the process of generating recommendations.

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Notes

  1. movielens.org

  2. http://grouplens.org/datasets/movielens/tag-genome/

  3. using polr in R

  4. Although in our user experiment we have three categorical levels for diversity, popularity, and serendipity, we decide to analyze the data based on continuous variables representing the diversity, serendipity and popularity of recommendation lists. This is because the distributions for diversity, popularity and serendipity are either skewed or highly overlapped, as shown in Appendix A. Thus using continuous variables makes our analyses independent from the distributions and easily duplicated.

  5. The statistical significance test reported is of the interaction effect only; i.e., of the difference between the odds-ratios of high- and low–introversion users

  6. The model is reported in the Appendix B Table 6

  7. This and the subsequent visualizations are generated as follows. First, we bucket the corresponding continuous score (in this case diversity score). Then, for each bucket, we compute the percentage of users who answered Just Right to the corresponding questions (in this case, the question assessing user preference for diversity). Thus, a dot represents the percentage of a personality-trait group per bucket. However, the line is fitted based on the continuous score, not on the bucketed percentages.

  8. The model is reported in Appendix B Table 7

  9. The model is reported in Appendix B Table 11

  10. These models are reported in Appendix B tables 8, 9, and 10.

    response ~ personality . level score (5)

  11. The model is reported in Appendix B Table 7.

  12. The model is reported in Appendix B Table 8

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Correspondence to Tien T. Nguyen.

Appendices

Appendix A Reason to exclude some users from the Analyses

Fig. 7
figure 7

The distributions of popularity scores

The score ranges from 0 to 1. Red indicates high level, blue medium level and green low level. There are three curves indicating the three peaks of high, medium and low levels of popularity.

Before explaining why we exclude some users from our main analyses, we discuss the results of our manipulation-check analyses, which reveal reasons for the exclusion.

Our manipulation-check analyses show that:

  • for the diversity metric, users perceived the differences in recommendations of high and low levels (p-value = 0.000), and of medium and low levels (p-value = 0.000). Users did not perceived the differences in recommendations of medium and high levels (p-value = 0.255).

  • for the popularity metric, users perceived the differences in recommendations of high and low levels (p-value = 0.000), in recommendations of medium and high levels (p-value = 0.000). Users also perceived the differences in recommendations of medium and low levels (with marginally p-value = 0.057).

  • for the serendipity metric, users perceived the differences in recommendations of high and low levels (p-value = 0.035), in recommendations of medium and low levels (p-value = 0.021). Users did not perceive the differences in recommendations with medium and high levels (p-value = 0.956).

We plot out the distributions of diversity, popularity, and serendipity to investigate why in some level comparisons (e.g. high vs. medium diversity level) users did not perceive the differences. Figures 7 and 8 show these distributions. In each distribution, we observe overlapping regions of high, medium and low levels. These overlapping make users not perceive differences in some recommendations with different levels.

Fig. 8
figure 8

The distributions of diversity scores (on the left) and serendipity scores (on the right)

Both scores range from 0 to 1. Red indicates high levels, blue medium levels and green low levels. In each figure, there are three curves indicating the three peaks of high, medium, and low levels of diversity (left figure) or of serendipity (right figure)

In this study, we want to examine the recommendation experience of users who perceived the differences in the diversity, popularity, or serendipity quantities per level. Thus, we remove from our analyses users to whom the recommendations cannot deliver to the appropriate quantities of diversity, popularity, and serendipity. Our process to remove users from the analyses is as follow.

Diversity

From Fig. 8 (left), there are clear cut-off points for low, medium and high levels of diversity at 0.24 and 0.41. Thus, we analyze 150/169 users who were assigned to the low-level with diversity scores less than 0.24, 147/176 users assigned to the medium-level with diversity scores from 0.24 to 0.41, and 171/176 users assigned to the high level with scores greater than 0.41.

Popularity

From Fig. 7, there are clear cut-off points for low, medium and high levels at 0.6% and 5.0%. Thus, we analyze 151/205 users assigned to the low-level with popularity scores from 0% to less than 0.6%, 135/195 users assigned to the medium-level with popularity scores from 0.6% to 5.0%, and 158/182 users assigned to the high-level with popularity scores greater than 5.0%.

Serendipity

From Fig. 8 (right), there are clear cut-off points for low, medium and high levels of serendipity at 0.46 and 0.80. Thus, we analyze 114/162 users who were assigned to the low-level with serendipity scores less than 0.46, 102/171 users assigned to the medium-level with serendipity scores from 0.46 to 0.80, and 92/199 users assigned to the high-level with serendipity score greater than 0.80.

Appendix B Tables

Table 6 Ordinal regression results for diversity preferences of introversion personality trait
Table 7 Ordinal regression results for popularity preferences of introversion personality trait
Table 8 Ordinal regression results for serendipity preferences of conscientiousness personality trait
Table 9 Ordinal regression results for serendipity preferences of introversion personality trait
Table 10 Ordinal regression results for serendipity preferences of openness personality trait
Table 11 Ordinal regression results for popularity preferences of conscientiousness personality trait (main effects)
Table 12 Ordinal regression results for users’ self-reported enjoyment with different diversity quantities for introversion personality trait
Table 13 Ordinal regression results for users’ self-reported enjoyment with different popularity quantities for emotional stability personality trait

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Nguyen, T.T., Maxwell Harper, F., Terveen, L. et al. User Personality and User Satisfaction with Recommender Systems. Inf Syst Front 20, 1173–1189 (2018). https://doi.org/10.1007/s10796-017-9782-y

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