Probabilistic recommendation of an item

1. A computer-implemented method of presenting a recommendation of an item on a user interface, the method comprising:

accessing, by an access module, unstructured data pertinent to attributes included in a first multi-dimensional vector, the first multi-dimensional vector representing a first product of which each item within the first multi-dimensional vector is a specimen, the unstructured data including an event record representative of a first event type pertinent to the first product;

calculating a probability based on the accessed unstructured data, the probability being of a co-occurrence of a second event type with the first event type, the second event type pertaining to a second product represented by a second cluster of items, the second cluster including the item to be recommended on the user interface, the calculating being performed by a processor of a machine;

calculating an argument of a maximum of the probability of the co-occurrence of the second event type with the first event type;

identifying, by a recommendation module, the item based on the calculated argument of the maximum of the probability of the co-occurrence;

storing metadata associated with the unstructured data mapping the second cluster of items to the first product at a database; and

causing a display of the user interface with the identified item at the user interface, the causing the display of the identified item including item data descriptive of the item and indicating the item as a specimen of the second product.

2. The computer-implemented method of claim 1 , wherein:

the first event type is a purchase of a specimen of the first product; and

the second event type is a purchase of a specimen of the second product.

3. The computer-implemented method of claim 1 , wherein:

the first event type is a bid on a specimen of the first product; and

the second event type is a bid on a specimen of the second product.

4. The computer-implemented method of claim 1 , wherein:

the first event type is a click to request information pertinent to the first product; and

the second event type is a bid on a specimen of the second product.

5. The computer-implemented method of claim 1 , wherein:

the first event type is a page view event referencing information pertinent to the first product; and

the second event type is a bid on a specimen of the second product.

6. The computer-implemented method of claim 1 further comprising:

accessing popularity data pertinent to the first product; and wherein

the identifying of the item is based on the popularity data.

7. The computer-implemented method of claim 1 , wherein:

the calculating of the probability includes generating a matrix based on the unstructured data, the matrix representing a pattern of co-occurrence pertinent to the first and second event types.

8. The computer-implemented method of claim 1 further comprising:

determining that an instance of the first event type and an instance of the second event type occurred within a threshold time period.

9. The computer-implemented method of claim 1 , wherein:

the unstructured data includes position data of a hyperlink presented in a web page, the hyperlink being to request information pertinent to the first product; and

the calculating of the probability is based on the position data.

10. The computer-implemented method of claim 1 further comprising

removing a portion of the unstructured data generated by a user, the removing being based on a number of activities performed by the user within a threshold time period.

11. The computer-implemented method of claim 1 further comprising

determining a rank of the item to be provided on a user interface; and wherein

the presenting of the recommendation is based on the rank of the item.

12. The computer-implemented method of claim 11 further comprising accessing a deadline of the item, the deadline indicating a time after which the item is unavailable for purchase; and wherein

the determining of the rank is based on the deadline of the item.

13. The computer-implemented method of claim 1 further comprising:

accessing a trust score of a seller of the item; and wherein

the identifying of the item is based on the trust score of the seller.

14. A system to present a recommendation of an item at a user interface, the system comprising:

an access module configured to access unstructured data pertinent to attributes included in a first multi-dimensional vector, the first multi-dimensional vector representing a first product of which each item within the first multi-dimensional vector is a specimen, the unstructured data including an event record representative of a first event type pertinent to the first product;

a hardware-implemented probability module configured to:

calculate a probability based on the accessed unstructured data, the probability being of a co-occurrence of a second event type with the first event type, the second event pertaining to a second product represented by a second cluster of items, the second cluster including the item to be recommended on the user interface; and

calculate an argument of a maximum of the probability of the co-occurrence of the second event type with the first event type;

a determination module configured to:

store metadata associated with the unstructured data mapping the second cluster of items to the first product at a database; and

a recommendation module configured to:

identify the item based on the calculated argument of the maximum of the probability of the co-occurrence; and

causing a display of the user interface with the identified item at the user interface, the causing the display of the identified item including item data descriptive of the item and indicating the item as a specimen of the second product.

15. The system of claim 14 , wherein:

the first event type is selected from a first group consisting of:

a purchase of a specimen of the first product;

a bid on a specimen of the first product;

a click to request information pertinent to the first product; and

a page view event referencing information pertinent to the first product; and

the second event type is selected from a second group consisting of

a purchase of a specimen of the second product; and

a bid on a specimen of the second product.

16. The system of claim 14 , wherein

the probability module, in calculating the probability, is to generate a matrix based on the unstructured data, the matrix representing a pattern of co-occurrence pertinent to the first and second event types.

17. A machine-readable storage device comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:

accessing unstructured data pertinent to attributes included in a first multi-dimensional vector, the first multi-dimensional vector representing a first product of which each item within the first multi-dimensional vector is a specimen, the unstructured data including an event record representative of a first event type pertinent to the first product;

calculating a probability based on the accessed unstructured data, the probability being of a co-occurrence of a second event type with the first event, the second event type pertaining to a second product represented by a second cluster of items, the second cluster including an item to be recommended on the user interface;

calculating an argument of a maximum of the probability of the co-occurrence of the second event type with the first event type;

identifying the item based on the calculated argument of the maximum of the probability of the co-occurrence;

storing metadata associated with the unstructured data mapping the second cluster of items to the first product at a database; and

causing a display of the user interface with the identified item at the user interface, the causing the display of the identified item including item data descriptive of the item and indicating the item as a specimen of the second product.

18. The non-transitory machine-readable storage medium of claim 17 , wherein:

the behavior data includes position data of a hyperlink presented in a web page, the hyperlink being to request information pertinent to the first product; and

the calculating of the probability is based on the position data.