An algorithm for ranking is developed. Based on the request, the recommendation system
finds the distance from this request to all documents from the collection of data. The request and
the collection of data are sets of features. The system ranks the results in accordance with the
following rules: minimizes the distance from the query to the relevant results, maximizes the
distance from the query to the irrelevant results and maximizes the distance between the relevant
query results. For ranking, Heterogeneous Euclidean-Overlap Metric (HEOM) of clothes
catalogue items is used. HEOM metric uses different attribute distance functions to measure
distances between objects in mixed scales.
A dataset of clothes catalogue items is collected. The system, in addition to the basic
attributes given as text descriptions of clothing, uses attributes based on expert description such as
fashion, psychological age and attractiveness. The dataset has features of text, linear and nominal
scales.
An algorithm for ranking is developed. Based on the request, the recommendation systemfinds the distance from this request to all documents from the collection of data. The request andthe collection of data are sets of features. The system ranks the results in accordance with thefollowing rules: minimizes the distance from the query to the relevant results, maximizes thedistance from the query to the irrelevant results and maximizes the distance between the relevantquery results. For ranking, Heterogeneous Euclidean-Overlap Metric (HEOM) of clothescatalogue items is used. HEOM metric uses different attribute distance functions to measuredistances between objects in mixed scales.A dataset of clothes catalogue items is collected. The system, in addition to the basicattributes given as text descriptions of clothing, uses attributes based on expert description such asfashion, psychological age and attractiveness. The dataset has features of text, linear and nominalscales.
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