- Custom embeddings based on actors, movie choices, music director choices
- User-user / item-item based on age, gender, persona
- Weekday, and weekend patterns of groups of listeners
- Location-based / time based - on the cab, commute to work, weekend
- Custom search-based keywords based
- Recency/relevance vs existing playlists
- User personalization/customization for new/old / English / regional language / Spiritual
- Balance between precompute / recent ranking
- Custom embeddings to find similar songs with lyrics, text, music
- A/B testing between paid / free users
- Conversions / increase time / auto-populate with more context info
- User affinity towards seasons / late-night sleep patterns/jogging music
- Millennials, Boomers, GenX, and Retired get some customization at each level
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Ref - Link
For each segment understand the preferences
- Seasonal Song
- Emotional Song
- Danceability
- Loudness
Popularity ranker: The first ranking algorithm is the popularity ranking, where we simply rank songs by popularity in descending order. we select the k most popular songs to show to the user
Relevance ranker: The second ranking algorithm is the relevance ranking, where we simply rank songs by their relevance to the user. Given a large pool of songs and a given user u, we select the k most relevant songs
Learned ranker: The third ranking algorithm is a model learned based on user preferences. We train a neural regression model that scores each song for a given user based on user-level, songlevel, and interaction-level features
- Next-item suggestion: predict user’s next action based on historical sequence.
- Next-in the basket: What will a user add to their cart e.g., on e-commerce sites, fast-food drive-thru.
- Session-based recommendation: Recommend items within short-term sessions e.g., on music streaming platform, social media etc.
- Getting inputs from user like stitch fix
Session-Level Information
- Time context. We use day of the week Dt and time of the day Ht. Note that even though, in Section 4, we partition sessions by using Dt only, both features are used for the model described in Section 5.
- Device context. In addition, we consider the device Yt used by the user to access the service at the beginning of a session. We restrict ourselves to the major devices: Y = {mobile, desktop, speaker, web, tablet}.
Current approaches experience difficulty with combining emotional features of the music to
the listener’s personality due to the fact that people’s perception of music genres is different
- Extroversion
- Agreeableness
- Consciousness
- Neuroticism
- Openness
- We can precompute and cache item representations for items when they become available in the catalog or for all items at once, as they only depend on item features
- Twitter is leveraging the Two Tower to combine earlier heuristics under a single umbrella model.
- Pinterest begins their post by exploiting a characteristic of the Two Tower architecture, the in-batch negative sampling, to generate a high number of negative examples which in return leads to better performance on several key metrics.
Merlin Models relies on the schema object to automatically build all necessary input and output layers.
Rather than relying on the scoring or retrieval models to infer this business logic and to recommend items appropriately, it’s necessary to add a Filtering stage to your recommender system.
NVIDIA-Merlin
Merlin is a framework providing end-to-end GPU-accelerated recommender systems, from feature engineering to deep learning training and deploying to production
Keep Exploring!!!
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