问题描述：
英语翻译
3.3.Neighborhood formation
The main goal of the neighborhood formation is to find for each customer x a set of
n customers that are the most similar to him.The similarity is given by the similarity
function sim.The neighbors are formed by applying proximity measures,such as thePearson
correlation (Sarwar et al.2000,Shardanand et al.1995),or cosine similarity (Good et al.
1999,Sarwar et al.2000) or mean squared differences (Shardanand et al.1995),between two
opinions or profiles of the customers.Given experiments done in other systems (Herlocker
et al.2000,Shardanand et al.1995),OSGS uses the mean squared differences proximity
measure.We apply it to (a) the history of the customers and to (b) their demographics and
preference profile in order to decide if customerx is a neighbor of customery .This is done
by applying a similarity function for each field of the profiles (see Lin 2002 for a detailed
description).Different similarity functions are used for each field in order to normalize
the fields’ values to the range [0,1].As stated above,the similarities of all the fields are
combined using the mean squared differences proximity measure in order to obtain one
weight for the profile (Shardanand et al.1995).
For example,in GMSIM the similarity function for the gender,age,quality,price,domain
expertise,and warranty fields is given by the following formula:
(Sarwar et al.2001)).OSGS constructs two neighborhoods for each customer,as described above.The idea behind this is that when a customer is searching for a specific product,it might be useful to assist him in finding the desired product by using information derived from customers who have similar tastes,or similar characteristics.
3.4.Weight functions
In order to prune the search tree effectively,each node in the search tree is assigned a weight
that estimates the customer’s interest in the category or product associated with the node.
In this section we present a description of the four weight functions that are used in our
algorithms.The weight functions are based on the customer’s preferences,the keywords
provided by the customer (in case of a keyword search),the customer’s history and his
neighbors (neighbors-by-history-profile and neighbors-by-demographics-and-preferenceprofile).
Table 1 summarizes all the weight functions and their notations.
是原文中几个段落,我翻不来,高手帮我翻译下.最好告诉我哪里有翻译原文,好的话追加100分
3.3.Neighborhood formation
The main goal of the neighborhood formation is to find for each customer x a set of
n customers that are the most similar to him.The similarity is given by the similarity
function sim.The neighbors are formed by applying proximity measures,such as thePearson
correlation (Sarwar et al.2000,Shardanand et al.1995),or cosine similarity (Good et al.
1999,Sarwar et al.2000) or mean squared differences (Shardanand et al.1995),between two
opinions or profiles of the customers.Given experiments done in other systems (Herlocker
et al.2000,Shardanand et al.1995),OSGS uses the mean squared differences proximity
measure.We apply it to (a) the history of the customers and to (b) their demographics and
preference profile in order to decide if customerx is a neighbor of customery .This is done
by applying a similarity function for each field of the profiles (see Lin 2002 for a detailed
description).Different similarity functions are used for each field in order to normalize
the fields’ values to the range [0,1].As stated above,the similarities of all the fields are
combined using the mean squared differences proximity measure in order to obtain one
weight for the profile (Shardanand et al.1995).
For example,in GMSIM the similarity function for the gender,age,quality,price,domain
expertise,and warranty fields is given by the following formula:
(Sarwar et al.2001)).OSGS constructs two neighborhoods for each customer,as described above.The idea behind this is that when a customer is searching for a specific product,it might be useful to assist him in finding the desired product by using information derived from customers who have similar tastes,or similar characteristics.
3.4.Weight functions
In order to prune the search tree effectively,each node in the search tree is assigned a weight
that estimates the customer’s interest in the category or product associated with the node.
In this section we present a description of the four weight functions that are used in our
algorithms.The weight functions are based on the customer’s preferences,the keywords
provided by the customer (in case of a keyword search),the customer’s history and his
neighbors (neighbors-by-history-profile and neighbors-by-demographics-and-preferenceprofile).
Table 1 summarizes all the weight functions and their notations.
是原文中几个段落,我翻不来,高手帮我翻译下.最好告诉我哪里有翻译原文,好的话追加100分
问题解答：
我来补答展开全文阅读