COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR BIDDING

1 . A computer-implemented method of successive bidding, the bidding being in relation to placement of an electronic advertisement on one or more electronically distributed content, the method comprising:

receiving, at a bidding server, a target cost per click (tCPC) and an information indicative of a “total amount of resource”;

receiving, by the bidding server from a first server, a request for a first bid for placing of the electronic advertisement on a first electronically distributed content;

transmitting, by the bidding server to the first server, a first bid value (bid 1 ) calculated by the bidding server, as:

bid 1 =adjustment factor 1 *tCPC*pCTR 1 ;

where:

adjustment factor 1 is equal to 1; and

pCTR 1 is a predicted click through rate corresponding to the first bid;

receiving, by the bidding server from the first server and/or a further server, a request for n th bid for placing of the electronic advertisement on the first electronically distributed content and/or a further electronically distributed content; and

transmitting, by the bidding server to the first server and/or the further server, a n th bid value (bid n ) calculated by the bidding server, as:

bid n =adjustment factor n *tCPC*pCTR n ;

where:

pCTR n is predicted click through rate corresponding to the n th bid;

adjustment factor n being determined by the bidding server, as:

adjustment





factor

n

=

adjustment





factor

(

n

-

1

)

*

(

1

-

α



(

eCPC

-

tCPC

eCPC

)

)

;

where:

eCPC is an effective cost per click;

n is greater than or equal to 2; and

α is a non-zero fraction having a value between 0 and 1.

2 . A computer-implemented method as claimed in claim 1 , wherein the value of α is dependent upon at least one of the “total amount of resource” and an “available amount of resource”.

3 . A computer-implemented method as claimed in claim 2 further comprising determining the “available amount of resource”.

4 . A computer-implemented method as claimed in claim 1 further comprising determining a value of the effective cost per click (eCPC).

5 . A computer-implemented method as claimed in claim 1 , wherein the adjustment factor n being determined by the bidding server, periodically.

6 . A computer-implemented method as claimed in claim 1 further comprising calculating the predicted click through rate (pCTR), as:

pCTR=β 1i *β 2j *β 3k . . . *β nm ;

where β 1i -β nm are independent features affecting the predicted click through rate.

7 . A computer-implemented method as claimed in claim 5 , wherein the features affecting the predicted click through rate include an advertiser related feature (β a,i ); an electronically distributed content related feature (β s,j ) and an electronic advertisement related feature (β c,k );

where β a,i is calculated by the bidding server, as:

β

a

,

i

=

∑

j

,

k



w

ijk

*

p

ijk

*

β

s

,

j

*

β

c

,

k

∑

j

,

k



w

ijk

*

β

s

,

j

2

*

β

c

,

k

2

;

where β s,j is calculated by the bidding server, as:

β

s

,

j

=

∑

i

,

k



w

ijk

*

p

ijk

*

β

a

,

i

*

β

c

,

k

∑

i

,

k



w

ijk

*

β

a

,

i

2

*

β

c

,

k

2

;

where β c,k is calculated by the bidding server, as:

β

c

,

k

=

∑

i

,

j



w

ijk

*

p

ijk

*

β

a

,

i

*

β

s

,

j

∑

i

,

j



w

ijk

*

β

a

,

i

2

*

β

s

,

,

j

2

;

and

where w ijk represents the number of impressions for an advertiser=‘i’, an electronically distributed content=‘j’, and an electronic advertisement=‘k’, and p ijk is a previously determined value of the click through rate for a combination of the advertiser ‘i’, the electronically distributed content ‘j’, and the electronic advertisement ‘k’.

8 . A computer-implemented method as claimed in claim 6 , comprising normalizing the electronically distributed content related feature (β s,j ) and the electronic advertisement related feature (β c,k ) for a new and/or an unknown site such that the new and/or unknown site has a β value of 1.

9 . A computer-implemented method as claimed in claim 6 , wherein the features affecting the predicted click through rate further include a slot size related feature (β slotsize,l ), a country related feature (β country,m ) and a time of the day related feature (β timeofday,n );

where β a,i is calculated by the bidding server, as:

β

a

,

i

=

∑

j

,

k

,

l

,

m

.

n



w

ijklmn

*

p

ijklmn

*

β

s

,

j

*

β

c

,

k

*

(

β

slotsize

,

l

)

*

(

β

country

,

m

)

*

(

β

timeofday

,

n

)

∑

j

,

k

,

l

.

m

.

n



w

ijklmn

*

β

s

,

j

2

*

β

c

,

k

2

*

(

β

slotsize

,

l

)

2

*

(

β

country

,

m

)

2

*

(

β

timeofday

,

n

)

2

;

where β s,j is calculated by the bidding server, as:

β

s

,

j

=

∑

i

,

k

,

l

,

m

,

n



w

ijklmn

*

p

ijklmn

*

β

a

,

i

*

β

c

,

k

*

(

β

slotsize

,

l

)

*

(

β

country

,

m

)

*

(

β

timeofday

,

n

)

∑

i

,

k

,

l

,

m

,

n



w

ijklmn

*

β

a

,

i

2

*

β

c

,

k

2

*

(

β

slotsize

,

l

)

2

*

(

β

country

,

m

)

2

*

(

β

timeofday

,

n

)

2

;

where β c,k is calculated by the bidding server, as:

β

c

,

k

=

∑

i

,

j

,

l

,

m

,

n



w

ijklmn

*

p

ijklmn

*

β

a

,

i

*

β

s

,

j

*

(

β

slotsize

,

l

)

*

(

β

country

,

m

)

*

(

β

timeofday

,

n

)

∑

i

,

j

,

l

,

m

,

n



w

ijklmn

*

β

a

,

i

2

*

β

s

,

j

2

*

(

β

slotsize

,

l

)

2

*

(

β

country

,

m

)

2

*

(

β

timeofday

,

n

)

2

;

where β slotsize,l is calculated by the bidding server, as:

β

slotsize

,

l

=

∑

i

,

j

,

k

,

m

,

n



w

ijklmn

*

p

ijklmn

*

β

a

,

i

*

β

s

,

j

*

β

c

,

k

*

(

β

country

,

m

)

*

(

β

timeofday

,

n

)

∑

i

,

j

,

k

,

m

,

n



w

ijklmn

*

β

a

,

i

2

*

β

s

,

j

2

*

β

c

,

k

2

*

(

β

country

,

m

)

2

*

(

β

timeofday

,

n

)

2

;

where β country,m is calculated by the bidding server, as:

β

country

,

m

=

∑

i

,

j

,

k

,

l

,

n



w

ijklmn

*

p

ijklmn

*

β

a

,

i

*

β

s

,

j

*

β

c

,

k

*

(

β

slotsize

,

l

)

*

(

(

β

timeofday

,

n

)

∑

i

,

j

,

k

,

l

,

n



w

ijklmn

*

β

a

,

i

2

*

β

s

,

j

2

*

β

c

,

k

2

*

(

β

slotsize

,

l

)

2

*

(

β

timeofday

,

n

)

2

;

where β timeofday,n is calculated by the bidding server, as:

β

timeofday

,

n

=

∑

i

,

j

,

k

,

l

,

m



w

ijklmn

*

p

ijklmn

*

β

a

,

i

*

β

s

,

j

*

β

c

,

k

*

(

β

slotsize

,

l

)

*

(

β

country

,

m

)

∑

i

,

j

,

k

,

l

,

m



w

ijklmn

*

β

a

,

i

2

*

β

s

,

j

2

*

β

c

,

k

2

*

(

β

slotsize

,

l

)

2

*

(

β

timeofday

,

n

)

2

;

where w ijklmn represents the number of impressions for an advertiser=‘i’, an electronically distributed content=‘j’, an electronic advertisement=‘k’, a slot size=‘l’, a country=‘m’, and a time of the day=‘timeofday’, and p ijklmn is the previously determined value of the click through rate for a combination of the advertiser ‘i’, the electronically distributed content ‘j’, the electronic advertisement ‘k’, a slot size ‘l’, a country ‘m’, and a time of the day ‘timeofday’.

10 . A computer-implemented method of bidding, the bidding being in relation to placement of an electronic advertisement on an electronically distributed content, the method comprising:

receiving, at a bidding server, a target cost per click (tCPC) and an information indicative of a “total amount of resource”;

receiving, by the bidding server from a server, a request for a first bid for placing of the electronic advertisement on the electronically distributed content;

transmitting, by the bidding server to the first server, a first bid value (bid 1 ) calculated by the bidding server, as:

bid 1 =adjustment factor 1 *t CPC* p CTR 1 ;

where:

adjustment factor 1 is equal to 1; and

pCTR 1 is a predicted click through rate corresponding to the first bid and is calculated as:

pCTR=β a,i *β s,j *β c,k ;

where β a,i represents an advertiser related feature affecting the predicted click through rate; β s,j represents an electronically distributed content related feature affecting the predicted click through rate; and β c,k represents an electronic advertisement related feature affecting the predicted click through rate;

where β a,i , is calculated by the bidding server, as:

β

a

,

i

=

∑

j

,

k



w

ijk

*

p

ijk

*

β

s

,

j

*

β

c

,

k

∑

j

,

k



w

ijk

*

β

s

,

j

2

*

β

c

,

k

2

;

where β a,j is calculated by the bidding server, as:

β

s

,

j

=

∑

i

,

k



w

ijk

*

p

ijk

*

β

a

,

i

*

β

c

,

k

∑

i

,

k



w

ijk

*

β

a

,

i

2

*

β

c

,

k

2

;

where β c,k is calculated by the bidding server, as:

β

c

,

k

=

∑

i

,

j



w

ijk

*

p

ijk

*

β

a

,

i

*

β

s

,

j

∑

i

,

j



w

ijk

*

β

a

,

i

2

*

β

s

,

j

2

;

and

where w ijk represents a number of impressions for an advertiser=‘i’, an electronically distributed content=‘j’, and an electronic advertisement=‘k’, and p ijk is the previously determined value of the click through rate for a combination of the advertiser ‘i’, the electronically distributed content ‘j’, and the electronic advertisement ‘k’.

11 . A bidding server for successive bidding, the bidding being in relation to a placement of an electronic advertisement on one or more electronically distributed content, the bidding server comprising:

a first receiving unit to receive a target cost per click (tCPC) and an information indicative of a “total amount of resource”;

a second receiving unit to receive from a first server, a request for a first bid for placing of the electronic advertisement on a first electronically distributed content, and to further revive from the first server and/or a further server, a request for n th bid for placing of the electronic advertisement on the first electronically distributed content and/or a further electronically distributed content;

a processor to calculate a first bid value (bid 1 ) and to calculate a nth bid value bid value (bid n );

wherein the first bid (bid 1 ) value is calculated as:

bid 1 =adjustment factor 1 * t CPC* p CTR 1 ;

where:

adjustment factor 1 is equal to 1; and

pCTR 1 is a predicted click through rate corresponding to the first bid; and

wherein the nth bid (bid n ) value is calculated as:

bid n =adjustment factor n * t CPC* p CTR n ;

where:

pCTR n is predicted click through rate corresponding to the n th bid;

adjustment factor n being determined by the bidding server, as:

adjustment





factor

n

=

adjustment





factor

(

n

-

1

)

*

(

1

-

α



(

eCPC

-

tCPC

eCPC

)

)

;

where:

eCPC is an effective cost per click;

n is greater than or equal to 2; and

α is a non-zero fraction having a value between 0 and 1.

a transmitter to transmit the first bid value to the first server and the nth bid value to the first server and/or the further server.

12 . A bidding server for successive bidding, the bidding being in relation to a placement of an electronic advertisement on one or more electronically distributed content, the bidding server comprising:

a first receiving unit to receive a target cost per click (tCPC) and an information indicative of a “total amount of resource”;

a second receiving unit to receive from a first server, a request for a first bid for placing of the electronic advertisement on a first electronically distributed content,

a processor to calculate a first bid value (bid 1 ) as:

bid 1 =adjustment factor 1 *t CPC* p CTR 1 ;

where:

adjustment factor 1 is equal to 1; and

pCTR 1 is a predicted click through rate corresponding to the first bid and is calculated as:

p CTR−β a,i *β s,j *β c,k ;

where β a,i represents an advertiser related feature affecting the predicted click through rate; ,β s,j represents an electronically distributed content related feature affecting the predicted click through rate; and β c,k represents an electronic advertisement related feature affecting the predicted click through rate;

where β a,i is calculated by the bidding server, as:

β

a

,

i

=

∑

j

,

k



w

ijk

*

p

ijk

*

β

s

,

j

*

β

c

,

k

∑

j

,

k



w

ijk

*

β

s

,

j

2

*

β

c

,

k

2

;

where β s,j is calculated by the bidding server, as:

β

s

,

j

=

∑

i

,

k



w

ijk

*

p

ijk

*

β

a

,

i

*

β

c

,

k

∑

i

,

k



w

ijk

*

β

a

,

i

2

*

β

c

,

k

2

;

where β c,k is calculated by the bidding server, as:

β

c

,

k

=

∑

i

,

j



w

ijk

*

p

ijk

*

β

a

,

i

*

β

s

,

j

∑

i

,

j



w

ijk

*

β

a

,

i

2

*

β

s

,

j

2

;

where w ijk represents the number of impressions for an advertiser=‘i’, an electronically distributed content=‘j’, and an electronic advertisement=‘k’; and a transmitter to transmit the first bid value to the first server, and p ijk is the previously determined value of the click through rate for a combination of the advertiser ‘k’, the electronically distributed content ‘j’, and the electronic advertisement ‘k’; and

a transmitter to transmit the first bid value to the first server.