Combining emotional analysis and product and service recommendation when recommending music playlist needs to be based on respect for user privacy and emotional needs, and pay attention to the fluency and naturalness of the user experience. The following is a step-by-step recommendation strategy framework, taking into account business transformation and the psychological comfort of female users： --- ### **1. Emotion recognition and classification** -**Technical implementation**： -Analyze user lyrics preferences and song list titles/descriptions (such as tags such as "decompression light music" and "Late night emo") through natural language processing (NLP). -Combine playback behavior (single cycle/skip rate/playback period) with physiological data (such as stress level and heart rate provided by wearable devices, user authorization is required). -Use emotional labeling models (such as pleasure/sadness/excitement/meditation) to cluster songs. -**Insights from female users**： -To avoid gender stereotypes (such as the default push of the "gentle healing" category), it needs to be dynamically matched by actual data. Scenarios can be subdivided: commuting, sports, parenting and relaxation, etc. --- ### **2. Sentiment-commodity service matching strategy** |**Emotions/scenarios **|**Examples of related products/services *|/**Recommended logic **| |----------------------|-----------------------------------------------|-----------------------------------------------------------------------------| | ** High-intensity stress ** / Meditation App members, hot spring vacation packages, ASMR sleep aid aromatherapy / Provide decompression solutions instead of directly promoting high unit price goods. | | * * Sports excitement ** / Limited-time discounts on sports headphones and women's fitness clothing / Fit the user's energy status and push practical products. | | ** Late-night loneliness **/ Online book clubs, psychological counseling hotline advertisements (ethical boundaries need to be cautious) / Focus on emotional companionship rather than consumption, and avoid taking advantage of fragile emotions. | | ** Weekend pleasure ** / Concert tickets, new products from Internet celebrity restaurants | High acceptance when users are in a positive mood, suitable for experiential consumption. | --- ### **3. Recommended contact design (to avoid intrusive feeling)** -**Natural implantation**： -The cover/banner of the song list is visually consistent with the emotional theme (for example, "The same essential oil purchase link" appears below the background image of the "Rain and White Noise Song List"). -Play intermittent voice recommendations (such as "the listeners of "Sunny" just now often buy these summer camping equipment..."). -**User control**： -Provide a "hide such recommendations" button, and mark "According to your recent "Refreshing Song List on Weekdays" recommendation, click to learn more". --- ### **4. Ethics and Data security** -**Transparency**： -Clearly inform the scope of use of emotional data (such as "we optimize recommendations through your playback records, you can manage permissions at any time"). -**Avoid manipulation**： -High-priced products are not recommended for sad emotions, and free content (such as psychological science articles) can be given priority. --- ### **5. Effect monitoring and Gender differentiation** -**A/B test**： -Compare the difference in the conversion rate of female users for "self-improvement services" (such as online courses) and "leisure consumption" under the same sentiment. -**Feedback mechanism**： -Join the "Reason for recommendation" questionnaire (such as "Is this ad valuable to you?") □Match the mood □The needs are accurate □ irrelevant"). --- By transforming emotions into contextualized needs, rather than forcibly associating consumption, it can not only improve business efficiency but also maintain brand trust. The focus is on making users perceive that they are "understood", not "analyzed."