Recommender systems have become integral to the user experience on many online platforms, such as Netflix and Amazon Prime Video. These systems often personalize not just the content but also the artwork representing that content. This research investigates the phenomenon of artwork misrepresentation and its effect on user trust and satisfaction. The study specifically focuses on how altered artwork, designed to attract user attention, can lead to perceptions of deception.

Background

Recommendation engines are ubiquitous across e-commerce and streaming platforms. Initially focused on accuracy, these systems have evolved to prioritize user satisfaction, trust, and engagement. Netflix, for instance, uses personalized artwork to increase user click-through rates. However, this practice raises concerns about misrepresentation when the artwork does not accurately reflect the content. This study explores these concerns through a user survey and data analysis.

Study Design

The study involved 15 participants who were shown film recommendations with varying levels of artwork misrepresentation. The recommendations were divided into five rounds, with each round presenting films with increasingly deceptive artwork. Participants' trust and satisfaction were measured using a combination of quantitative surveys and qualitative feedback.

In the initial round, participants viewed films with their original artwork. In subsequent rounds, the artwork was progressively altered, starting with minor changes like color adjustments and ending with completely unrelated images. The aim was to assess how these changes affected participants' trust in the recommendation system and their satisfaction with the selected films.

The figure above shows a sample recommendation from Round 1 with the original artwork provided by content creators.

The figure above shows recommendations in Round 4, where the artwork has been highly altered to misrepresent the actual content.

General Results

The results indicate that users quickly detect deceptive artwork, leading to reduced trust and satisfaction. Significant correlations were found between trust, satisfaction, and the level of artwork misrepresentation. Participants reported feeling deceived when the artwork significantly differed from the film's actual content, resulting in lower trust and satisfaction scores.

Interestingly, the study found that minor changes, such as color adjustments, did not significantly impact user trust or satisfaction. However, more significant alterations, such as the use of unrelated images, were quickly detected and negatively impacted user perceptions.

Analysis of Deceived vs. Non-Deceived Participants

Participants who were deceived by altered artwork experienced lower satisfaction and trust compared to those who were not deceived. The study highlights the importance of maintaining accurate and representative artwork to preserve user trust. The data showed that non-deceived participants maintained a relatively high level of trust and satisfaction throughout the study, whereas deceived participants reported a sharp decline in both metrics.

The above figure shows the correlation between satisfaction and trust, highlighting the impact of artwork misrepresentation.

The figure above indicates participants’ recommendation choices and the standard deviation of the results.

Discussion

The discussion emphasizes that while some level of artwork personalization can enhance user engagement, excessive misrepresentation leads to decreased trust and satisfaction. The study suggests that minor changes to artwork are acceptable, but significant alterations that mislead users are detrimental. The findings align with previous research indicating that transparency and accuracy in recommendation systems are crucial for maintaining user trust.

Additionally, the study explores the concept of serendipity in recommendations. While the goal of personalized artwork is to create pleasant surprises for users, the results show that deception undermines this objective. Users appreciate unexpected but relevant recommendations, not those that mislead or confuse them.

The figure above shows the relationship between expectations and relevance, emphasizing the importance of accurate artwork representation.

Conclusion

The study concludes that recommendation systems should prioritize user satisfaction by maintaining the integrity of content presentation. While personalized artwork can enhance user engagement, it is crucial to avoid significant misrepresentation that can lead to distrust and dissatisfaction. Future research should explore subtle artwork changes and involve a more diverse participant pool to validate these findings.

Overall, the study highlights the delicate balance recommendation systems must strike between personalization and accuracy. Ensuring that artwork accurately reflects the content is essential for maintaining user trust and satisfaction in the long term.

The figure above summarizes the overall satisfaction of participants with the provided recommendations.