A recommendation is a formal or informal proposition for action.
- Recommendation Ontology (vocabulary): http://purl.org/ontology/rec/core#
- Recommendation Ontology (OWL): http://smiy.sourceforge.net/rec/rdf/recommendationontology.owl
- Recommendation Ontology (n3): http://smiy.sourceforge.net/rec/rdf/recommendationontology.n3
To recommend is the act of making a formal or informal recommendation.
It involves one party informing another party about their interest in a given subject or object, and strongly (or softly) advising or suggesting that the other party look into, make use of, research, or (in the example of a film) view the subject or object of interest.
Types of Recommendation
A formal proposition for action is any propostion that indicates a pre-arranged level of expectation that the proposed action be fulfilled by the proposee. The proposition is usually well-documented and noterized by the parties involved through a binding contract. Additionally, it may also include prerequisites or strict stipulations under which the proposition is carried out. Business Proposals are prime examples of formal propostions. The most basic real-world example would be that of a solicited recommendation, where a buyer pays an expert for a formal analysis of options in a given domain (i.e. a home buyer pays a Real Estate Agent for their assitance in finding a home, or, an enviro-conscious tourist looks to plan their next vacation through a Travel Agent with a reputation for planning environmentally sustainable getaways with green resorts).
An informal proposition for action is any propostion that does not require a pre-arranged level of expectation that the proposed action be fulfilled by the proposee. This type of proposition is typically neither well-documented nor noterized by the parties involved. Additionally, informal propositions do not include prerequisites or strict stipulations under which the proposition is carried out. Suggestions are prime examples of informal propostions. The most basic real-world example would be that of an unsolicited recommendation, where one person offers advice to another in an attempt to influence their decision-making or selection process (i.e. a music enthusiast suggests that his friends come with him to check out a new band's gig on the coming weekend, or, a "foodie"/restaurant-goer gives his visiting relatives a list of the Top 5 "must eat at" restaurants in his city).
1. John, a TV serial buff tells Mary that she might like the latest episode of a particular show, because her favorite actress is guest-starring in it.
John has made a recommendation. In this case it is an informal proposition, the proposed action being that Mary view a particular episode of a specific Television show.
2. John works for a government agency and upon request from Mary, is sent to inspect her television set to verify if it will be "Digital capable" after the pending Analog Shut Down. Based on his inspection, John creates a report which advises Mary to either upgrade her television's capacitor, purchase an extra set-top box which will receive digital signals and convert them to analogue, or replace the television set completely in order to fully support digital broadcasts.
John has made some recommendations. In this case, each recommendation represents a formal proposition, the proposed action being that Mary carry out one of three specific options for upgrading her Analog TV set (wikipedia:Analog TV) to a Digital-capable set (wikipedia:Digital TV). John's recommendation is a formal one, not only because it was solicited by Mary, but because John is publicly known and trusted to be an expert in the area in which the recommendation was solicited. Trust in this case, was created by the reputation of the backing organization (the government) and the authentication of John's identity (through his official government badge and ID card).
- Google +1 button: http://www.google.com/+1/button/
- Android TV - Recommending TV Content: http://developer.android.com/training/tv/discovery/recommendations.html
- Netflix Recommendations -- Beyond the 5 stars (Part 1): https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429
- Recommending Videos that Engage Your Viewers: https://medium.com/jw-player-engineering/recommending-videos-that-engage-your-viewers-cbe5e7a68fc0
- How to Improve Movie and Show Recommendations on Google TV: https://www.howtogeek.com/723700/how-to-improve-movie-and-show-recommendations-on-google-tv/
- Tracking the Worst Sci-Fi Movies With Angular and Slash GraphQL: https://dzone.com/articles/tracking-the-worst-sci-fi-movies-with-angular-and
- Angie's List - Service Company Reviews & Ratings (in Craig's List style): http://www.angieslist.com/
- wikipedia:Angie's List
- Yelp - Web 2.0 Social Networking & User Reviews company: http://www.yelp.com
- Yelp Mobile: http://mobile.yelp.com/
- wikipedia:Yelp, inc.
- RISE project - Recommendation types: http://www.open.ac.uk/blogs/RISE/2011/04/19/recommendation-types/
- Tag-Based User Profiling for Social Media Recommendation: http://www.aaai.org/Papers/Workshops/2008/WS-08-06/WS08-06-006.pdf
- How can we best measure how good people think our recommendations are?: http://blog.notu.be/2010/05/13/how-can-we-best-measure-how-good-people-think-our-recommendations-are/
- 6 Ways to Boost Your Rankings Using Google Authorship: http://searchenginewatch.com/article/2170855/6-Ways-to-Boost-Your-Rankings-Using-Google-Authorship (GRAPH showing 90% of people trust friends' recommendations, compared to < 60% for TV/Radio/Newspaper)
- Search & Recommendations keep visitors on pages/site longer: http://www.rovicorp.com/dm/data/searchandrecommendation/thankyou.html
- Where's the streaming video you really want to see? Good question: https://www.cnet.com/news/streaming-video-survey-ibm-netflix-amazon-prime-video-hulu/ (more than 2/3 of respondents said lack of content and poor recommendations limits usefulness of paid streaming video services)
- Study - Readers Distrust Sites That Use (external) Content Recommendation Widgets: https://www.mediapost.com/publications/article/325639/study-readers-distrust-sites-that-use-content-rec.html
- Piano Introduces AI-Backed Content Recommendation Platform For Newsletters, Email: https://www.mediapost.com/publications/article/332519/piano-introduces-ai-backed-content-recommendation.html
- BBC developing 'public service algorithm' for Sounds app to avoid echo chambers: https://www.pressgazette.co.uk/bbc-developing-public-service-algorithm-for-sounds-app-to-avoid-echo-chambers/
- Meredith Acquires 'Swearby,' Digital Recommendations Platform: https://www.mediapost.com/publications/article/345490/meredith-acquires-swearby-digital-recommendatio.html
- The Recommendation Ontology: http://smiy.wordpress.com/2010/08/07/the-recommendation-ontology/
- Google's +1: A Facebook "Like" copycat that falls short on search enhancement: http://www.zdnet.com/blog/google/googles-1-a-facebook-like-copycat-that-falls-short-on-search-enhancement/2856
- Growing Android TV engagement with search and recommendations: http://android-developers.blogspot.ca/2015/06/growing-android-tv-engagement-with.html