Emosic: A Mood-Based Music Recommendation Plug-in Embedded Into Spotify

An AI-driven plug-in for music streaming players, based on research in Human-AI interaction, aims to comprehend music users' awareness of AI and is designed following the guidelines of AI design ethics.
Role
UI Designer, UX Researcher
Duration
Feb, 2022 - Jun, 2022
TEAM
Ariel Xiong, Iris Xu, Peggy Hu, Hingis Chang
Tool
Figma

In laying the groundwork for our design, our team delved into an extensive literary review. Our objective was twofold: to grasp the intricate relationship between music and mood, and to glean insights that could shape the trajectory of our product development.

We approached the review with a structured focus, dividing our inquiry into several key themes:

Data Collection Strategies:

Central to our investigation was understanding how AI models gather user feedback within music streaming applications. We explored various feedback systems, evaluating their efficiency and applicability to our product. This analysis guided our decisions on implementing robust mechanisms for soliciting and processing user input.

AI Awareness and Transparency:

As AI increasingly permeates our digital experiences, we scrutinized how users interact with and perceive AI-driven platforms. Balancing the need for personalized recommendations with user privacy and transparency emerged as a critical consideration. Our findings informed the design ethos of our plug-in, emphasizing clear communication and user empowerment.

Psychological Insights:

Delving into psychological research, we unearthed nuanced understandings of the interplay between mood and music. We examined prevailing mood models and their implications for categorizing and recognizing human emotions. This deep dive enabled us to refine our algorithm's capacity to discern and cater to diverse emotional states, enhancing user engagement and satisfaction.

Current Methodologies in Music Recommendation:

Grounded in technical inquiry, we dissected prevailing methodologies underpinning music recommendation algorithms. Understanding the intricacies of data processing and model training allowed us to optimize our plug-in's performance. Moreover, we identified constraints inherent to AI models, ensuring our design process remained cognizant of ethical and practical considerations.

By synthesizing insights from our literary review, we forged a robust foundation for our product development journey. Armed with a nuanced understanding of user behavior, psychological principles, and technical methodologies, we embarked on the creation of a music recommendation plug-in poised to revolutionize the digital listening experience.

Competitor analysis among current music apps

Competitive Analysis between KKBOX, Spotify, Apple Music, Amazon Music

Problem Statement

Users can’t explore the music playlist fitting their moods easily with existing music streaming applications.

User Research

Wireframes

Consider entrance/Touch points for users

Touchpoint #1: Leveraging Push Notifications for Enhanced User Engagement

Objective:

The primary objective of this touchpoint is to increase user clicks and interactions within the music streaming software by utilizing push notifications effectively. By doing so, we aim to improve user engagement, promote feature discovery, and ultimately drive overall user satisfaction and retention.

Touchpoint #2: Interactive Homepage Pop-Up for Quick Engagement

Objective:

The primary objective of this touchpoint is to provide users with an immediate opportunity for engagement upon entering the music app, without requiring significant time or effort. By offering quick and enjoyable activities through a pop-up window on the homepage, we aim to capture users' attention, spark their curiosity, and create a welcoming and interactive environment that encourages further exploration of the app.

Touchpoint #3: Personalized Quiz Link Integration for Active Homepage Browsing

Objective:

The primary objective of this touchpoint is to provide users with personalized music recommendations and foster exploration of new content while they are actively browsing the homepage. By integrating a quiz link into the sliding content, we aim to capitalize on users' interest in discovering new music and offer them a tailored and engaging experience that encourages further exploration and discovery within the app.

Solutions Proposals

By synthesizing insights from our literary review, we forged a robust foundation for our product development journey. Armed with a nuanced understanding of user behavior, psychological principles, and technical methodologies, we embarked on the creation of a music recommendation plug-in poised to revolutionize the digital listening experience.

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