Fast method for renewal and associated recommendations for market basket items

• US 20020143613A1
• Filed: 02/05/2001
• Published: 10/03/2002
• Est. Priority Date: 02/05/2001
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Belitskaya, Ilana, Natarajan, Ramesh, Hong, Se June
• Application Number:
  US09/773,809
• Publication Number:
  US 20020143613A1
• Time in Patent Office: Days
• US Class Current: 705/14
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]

Abstract

When a customer is in the process of filling a market basket for purchase on an Internet commerce site, a method makes prioritized recommendation of items so as to maximize the likelihood that the customer will add to the basket those items that are in the list with higher priorities. The method separately considers in turn preferences due to a current set of items in the market basket and also preferences due to a new choice independent of what is in the market basket. In this way, the method recognizes that not all items in the market basket are selected because of their affinity with some other item already in the basket. The two preferences are estimated separately from training data and combined in proper proportions to obtain an overall preference for item not yet in the market basket.

Claims

1. A method for making prioritized recommendations to a customer in the process of filling a market basket for purchase on an Internet commerce site, the method comprising the steps of:

generating a matrix of training data;

considering preferences based on associative and renewal buying history from the training data; and

making a prioritized recommendation of items so as to maximize the likelihood that the customer will add to the market basket those items with higher priorities.

2. The method of claim 1, wherein the two preferences are estimated separately from the training data and combined in proper proportions to obtain an overall preference for item not yet in the market basket.

3. A method for making prioritized recommendations to a customer in the process of filling a market basket for purchase on an Internet commerce site, the method comprising the steps of:

collecting statistics from training data;

precomputing model parameters from the collected statistics; and

recommending ordering for a given partial market basket based on the precomputed model parameters.

4. The method of claim 3, wherein the step of collecting statistics comprises the steps of:

(a) for each item j, obtaining n_j a number of baskets with item j purchased;

(b) for each item j, obtaining n_j′

a number of baskets with j being a sole item purchased;

(c) for each pair of items i and j, obtaining a number of market baskets n_ji with items j and i purchased together; and

(d) for each pair of items i and j, obtaining a number of market baskets n_ji′

with items i and j being the only two items purchased.

5. The method of claim 4, wherein the step of precomputing model parameters comprises the steps of:

6. The method of claim 5, wherein given a partial basket B−

{i₁, i₂, . . . , i_k} and {overscore (B)} is a complementary set of items not in B, the step of recommending ordering for a given partial market basket comprises the steps of;

(a) if B is empty, sorting items in order of decreasing P(j|renewal) and returning this as an item preference ordering;

(b) if B is non-empty, then

(i) computing P(renewal|B)=min<sub>ikε

B</sub>P(renewal|i_k);

(ii) compute a normalization factor

$\underset{k\in <br><br>\stackrel{\_}{B}}{\sum <br><br>}<br><br>P^{\mathrm{\prime <br><br>}}<br><br>\left(k|\mathrm{renewal}\right);<br><br>$

(iii) for each item jε

{overscore (B)}, computing

$P<br><br>\left(j|\mathrm{renewal}\right)=\frac{P^{\mathrm{\prime <br><br>}}<br><br>\left(j|\mathrm{renewal}\right)}{\underset{k\in <br><br>\stackrel{\_}{B}}{\sum <br><br>}<br><br>P^{\mathrm{\prime <br><br>}}<br><br>\left(k|\mathrm{renewal}\right)};<br><br>$

(iv) computing a normalization factor

$\underset{k\in <br><br>\stackrel{\_}{B}}{\sum <br><br>}<br><br>P^{\mathrm{\prime <br><br>}}<br><br>\left(j|a<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>o,B\right);<br><br>$

(v) for each item jε

{overscore (B)}, computing

P′

(j|asso,B)=max<sub>ikε

B</sub>P(j|asso,i_k);

(vi) for each item jε

{overscore (B)}, computing

$P<br><br>\left(j|a<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>o,B\right)=\frac{P^{\mathrm{\prime <br><br>}}<br><br>\left(j|a<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>o,B\right)}{\underset{k\in <br><br>\stackrel{\_}{B}}{\sum <br><br>}<br><br>P^{\mathrm{\prime <br><br>}}<br><br>\left(k|a<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>s<br><br>\text{\hspace{1em}<br><br>}<br><br>o,B\right)};<br><br>$

(vii) for each item jε

{overscore (B)}, computing

P(j|B)=P(j|asso,B)P(asso|B)+P(renewal|B);

 

and

(viii) sorting items in order of decreasing P(j|B) and returning this as an item preference ordering.

7. The method of claim 6, wherein the step of sorting comprises the step of using a final probability obtained for each item, P(j|B), of a customer buying the item to maximize profit by recommendation.

8. The method of claim 7, wherein the step of using a final probability of an item to maximize profit comprises the steps of:

assigning a profit amount, $_j, to each item;

computing P(j|B)$_j for each item; and

ranking recommendations based on the computation of P(j|B)$_j for each item.