Enhanced Bidding System
Some embodiments provide a bidding system configured to estimate a bid for a new item. In some implementations, the bidding system can be configured to build a statistics model for predicting a price difference between the listed price of an item and sold price of the same item. In some embodiments, the bidding system can be configured to train a classification model using extracted features. The prediction can be based on sales information regarding one or more items that were previously sold. In some implementations, building such a statistics model may include extracting features from structured data and as well as unstructured data regarding those previously sold items. Structured data may include one or more classifications of the items that are readily available in a classification system or classification systems. Unstructured data may include text description about those items.
1 . A system for generating a recommendation for suggestion to a user for bidding a target item, the system comprising a processor configured to execute computer programs such that when the computer programs are executed, the system is caused to perform:
obtaining data regarding one or more items in a same category of the target item, wherein the data includes structured data and unstructured data;
processing the data regarding one or more items to extract a set of features for each of the one or more items;
training a regression model using the sets of features extracted for the one or more items;
obtaining data regarding the target item, the data regarding the target item indicating a listing price of the target item; and
predicting a sale price using the listing price of the target item and the regression model.
2 . The system of claim 1 , wherein processing the data regarding one or more items includes text mining the unstructured data to extract at least one of one or more features.
3 . The system of claim 2 , wherein the text mining of the unstructured data includes removing one or more stop words in the unstructured data and generating one or more nGrams.
4 . The system of claim 2 , wherein the text mining of the unstructured data includes using a frequency-inverse document frequency technique to find the at least one feature in unstructured data.
5 . The system of claim 1 , wherein the regression model includes at least one of a random forest and a boosting tree.
6 . The system of claim 1 , wherein the sets of features extracted for the one or more items indicate a number of days (DOM) on a market for sale for each of the one or more items, and the method further comprises:
training a classification model using the sets of features extracted for the one or more items; and
predict DOM for the target item using the classification model.
7 . The system of claim 1 , wherein the classification model includes at least one of a logic regression model, a random forest, and a support vector machine.
8 . The system of claim 1 , further comprising determining the one or more items are in the same category as the target item by virtue of each of the one or more items is located a same geographic area as the target item.
9 . The system of claim 1 , wherein the unstructured data includes text description about the one or more items.
10 . A method for generating a recommendation for suggestion to a user for bidding a target item, the method being implemented in a processor configured to execute computer programs, the method comprising:
obtaining data regarding one or more items in a same category of the target item, wherein the data includes structured data and unstructured data;
processing the data regarding one or more items to extract a set of features for each of the one or more items;
training a regression model using the sets of features extracted for the one or more items;
obtaining data regarding the target item, the data regarding the target item indicating a listing price of the target item; and
predicting a sale price using the listing price of the target item and the regression model.
11 . The method of claim 10 , wherein processing the data regarding one or more items includes text mining the unstructured data to extract at least one of one or more features.
12 . The method of claim 11 , wherein the text mining of the unstructured data includes removing one or more stop words in the unstructured data and generating one or more nGrams.
13 . The method of claim 11 , wherein the text mining of the unstructured data includes using a frequency-inverse document frequency technique to find the at least one feature in unstructured data.
14 . The method of claim 10 , wherein the regression model includes at least one of a random forest and a boosting tree.
15 . The method of claim 10 , wherein the sets of features extracted for the one or more items indicate a number of days (DOM) on a market for sale for each of the one or more items, and the method further comprises:
training a classification model using the sets of features extracted for the one or more items; and
predict DOM for the target item using the classification model.
16 . The method of claim 15 , wherein the classification model includes at least one of a logic regression model, a random forest, and a support vector machine.
17 . The method of claim 10 , further comprising determining the one or more items are in the same category as the target item by virtue of each of the one or more items is located a same geographic area as the target item.
18 . The method of claim 10 , wherein the unstructured data includes text description about the one or more items.