Sentence examples for collaborative recommendations from inspiring English sources
The conflict intensified such that several participating environmental groups, along with the Nez Perce Indian Tribe, withdrew from the state-level advisory group attempting to provide collaborative recommendations.
In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations.
16 Efficacy endpoints were analysed in accordance with the European League Against Rheumatism EULARR)/ACR collaborative recommendations for reporting RA disease activity in clinical trials 18 and included the mean change in DAS28-ESR, ACR20/50/70/90 response and ACR core components.
In the collaborative recommendation approaches [17, 20, 30], the system recommends alternatives queries to the current user using his query and the query log containing previous user's queries.
Collaborative recommendation has attracted various research works in recent years.
It's not perfect - it thinks distorted guitar is the same as harpsichord, for example - but it is far more accurate than collaborative recommendation.
Collaborative recommendation (CR) is a fundamental enabling technology for providing high-quality personalization services in various online and offline applications.
It incorporates a mixture of content-based and collaborative recommendation strategies, thus avoiding the drawbacks of each individual method, and is able to perform recommendations in heterogeneous scenarios.
However, most previous works on collaborative recommendation only focus on exploiting homogeneous explicit feedbacks such as grade scores or homogeneous implicit feedbacks such as clicks or purchases.
We coin this problem heterogeneous collaborative recommendation (HCR) from the perspective of users' heterogeneous feedbacks of implicit examinations and explicit ratings.
Collaborative recommendation with heterogeneous explicit feedbacks (CR-HEF) such as 5-star grade scores and like/dislike binary ratings is a new and important problem, because it provides a rich and accurate source for learning users' preferences.
