Product Screening to Multicustomer Preferences: Multiresponse Unreplicated Nested Super-ranking
نویسندگان
چکیده
منابع مشابه
Ranking Needs Preferences of Iranian Seafarers
People are motivated according to their needs. These needs vary according to the time and based on various situations. Identifying and assessing human needs are the first and vital step in determining the amount of its impact on work and activity. According to these facts, the aim of this research is to identify the needs of the Iranian seafarers on board merchant ships. It also tries to spec...
متن کاملFrom Incomplete Preferences to Ranking via Optimization
We consider methods for aggregating preferences that are based on the resolution of discrete optimization problems. The preferences are represented by arbitrary binary relations (possibly weighted) or incomplete paired comparison matrices. This incomplete case remains practically unexplored so far. We examine the properties of several known methods and propose one new method. In particular, we ...
متن کاملCollaborative Ranking for Local Preferences
For many collaborative ranking tasks, we have access to relative preferences among subsets of items, but not to global preferences among all items. To address this, we introduce a matrix factorization framework called Collaborative Local Ranking (CLR). We justify CLR by proving a bound on its generalization error, the first such bound for collaborative ranking that we know of. We then derive a ...
متن کاملLabel Ranking by Learning Pairwise Preferences Label Ranking by Learning Pairwise Preferences
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to...
متن کاملAdapting Document Ranking to Users' Preferences Using Click-Through Data
1* Min Zhao is currently researcher at NEC Lab China, Beijing. Abstract. This paper proposes a new approach to ranking the documents retrieved by a search engine using click-through data. The goal is to make the final ranked list of documents accurately represent users’ preferences reflected in the click-through data. Our approach combines the ranking result of a traditional IR algorithm (BM25)...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Quality, Statistics, and Reliability
سال: 2008
ISSN: 1687-7144,1687-7152
DOI: 10.1155/2008/156851