Tavilm

With the endless number of movies available across multiple platforms, users often feel overwhelmed and struggle to decide what to watch. This indecision, commonly known as choice paralysis, causes frustration and leads to wasted time and lower satisfaction.

As I explored this problem, I wanted to understand:

• Why do people struggle to pick a movie even when they already know their favorite genre?
• What factors influence how people decide which movie to watch?
• How can design help reduce the effects of choice paralysis without removing freedom of choice?

Tavilm is designed to reduce choice paralysis through a personalized, AI-powered recommendation system that presents users with a curated range of films — just enough to make choosing easier without feeling limited.

Key features include:

• Smart Curation: Recommends movies based on personal preferences like genre, cast, and soundtrack.
• Movie Generator: Randomly suggests a curated list when users can’t decide what to watch.
• Movie Tinder: Lets users swipe between films and receive more accurate recommendations over time.
• Social Connection: Enables users to add friends, view what they’re watching, and discuss movies.

1. Discovering the Problem

After initial brainstorming, I identified the main challenge:

“ Which Movie Should I Watch? ”

To understand the root of this issue, I asked guiding questions such as:

• Why do people struggle to decide what movie to watch?
• What do they usually do before deciding?
• What internal and external factors influence their decision?

From here, I conducted desk research (via academic journals, articles, and behavioral studies) and user interviews to understand how people make movie choices and what makes them indecisive.

2. Research Findings

A. From Literature and Online Sources

• Too many options create choice paralysis — even when users already know their preferred genre.
• Internal factors such as personal preferences (genre, actor/director, soundtrack, perceived value, attention span, and personality) have the strongest influence.
• External factors such as ratings, reviews, word of mouth, critics, influencers, social relevance, and platform availability also play significant roles.
• Before choosing a film, people often check trailers, posters, and reviews as part of their decision-making ritual.

This revealed that choice paralysis isn’t caused by indecision alone — it’s also about information overload and social validation.

source: https://www.mdpi.com/2071-1050/13/11/5920

B. From User Interviews

To verify these findings, I interviewed several users who regularly watch movies but often struggle to pick one.

Key insights:

• Gen Z users are more prone to choice paralysis than Millennials, due to naturally indecisive tendencies and social comparison behavior.
• Trailers have less influence than genre, actor, and reviews when deciding what to watch.
• Many users experience FOMO (Fear of Missing Out) and tend to watch trending movies to stay included in social conversations.
• Word of mouth has a major influence, with most users trusting friends’ recommendations over algorithms.

3. Deep Analysis and Synthesis

From both research and interviews, I mapped the causes and potential solutions to choice paralysis:

• Internal Triggers: Personality, mood, and taste-based preferences.
• External Triggers: Ratings, reviews, word of mouth, and social pressure.
• Behavioral Pattern: Users seek reassurance before deciding — they look for confirmation through trailers, reviews, or friends’ opinions.

Key Insight:

The main reason users struggle isn’t lack of options, but too many options without meaningful differentiation.

According to Camerer’s study, the ideal number of options lies between 8–15, where users experience the highest satisfaction and least cognitive load. Anything more increases hesitation and regret.too many options without meaningful differentiation.

4. Defining the Design Direction

To reduce decision fatigue, I focused on implementing strategies such as:

• Limiting the number of visible options to the ideal range.
• Providing personalized curation that reflects both internal and external influences.
• Simplifying comparison by emphasizing key information: genre, cast, and rating.
• Encouraging spontaneous decisions through features like Movie Tinder and Movie Generator.

The goal was not to restrict freedom, but to curate clarity — helping users reach confident decisions faster.

The visual design of Tavilm embraces a cinematic yet modern aesthetic to evoke the atmosphere of exploring a theater — intimate, immersive, and intuitive. I used a dark background to emphasize film content while maintaining focus and reducing eye strain during browsing.

For the primary color, I used #FAC000, a vivid yellow tone that symbolizes warmth, energy, and excitement — reminiscent of cinema lights and the feeling of anticipation before a movie starts. To maintain visual hierarchy and flexibility, I developed a color scale ranging from 50 to 950, allowing balanced contrasts between highlights and darker UI elements.

The typography combines SF Pro for its legibility and neutral elegance, creating a modern and friendly tone that suits both light and dark interfaces. The use of ample spacing, rounded corners, and minimalistic iconography helps users focus on the content rather than the interface itself — reinforcing Tavilm’s goal to simplify decision-making.

Tavilm successfully redefined the movie discovery experience by turning indecision into engagement. Through AI-driven curation, simplified choice presentation, and social integration, users could find films faster, decide with confidence, and enjoy higher satisfaction.

The project reinforced my belief that reducing friction in decision-making is not about removing options, but about guiding users through meaningful ones — an insight I continue to apply in every design challenge.

Through Tavilm, I learned how behavioral design and data-driven insights can work together to solve emotional challenges like choice paralysis. This project also taught me the importance of balancing autonomy and algorithmic guidance, ensuring technology empowers users rather than overwhelms them.