Dimensions of Analysis
- Recommendation Context
- Whose Opinions
- Personalization Level
- Privacy and Trustworthiness
- Recommendation Algorithms
A domain may refer to:
- News: the news in 今日头条
- Products: a book recommended by Amazon
- Matchmaking: online matching system in DotA 2 / 王者荣耀
- Sequences: music playlists recommended by Netease Music
Recommender should consider choosing whether a new item or new or a re-recommend old one.
- Sales or other benefits for recommendations themselves (most of the time)
- Education of user / customer (very rarely)
- Some other nonsences
What's the user doing at the time of recommendation? Shopping, listening to music or reading news?
- Experts: wine shop with comprehensive reviews that only experts can provide
- Ordinary "phoaks"
- People like you: movie recommendations on Douban are based-on users' whose taste is similar with you
Privacy and Trustworthiness
- Privacy: who knows what about me and my data on the recommender system?
- Trustworthiness: Is the recommendation honest? Or business rules built-in by operator for commercial benefits?
Predictions? Recommendations? Explicit or implicit input?
See Recommendation Algorithms.