Search with autosuggest and refinements
Techniques for search with autosuggest and refinements are disclosed. In some embodiments, search with autosuggest includes determining a plurality of potential query suggestions for a partially entered query string; and automatically suggesting a plurality of queries based on a query count for each of the queries. For example, the query count can correspond to a popularity of the query. In some implementations, the query count can be determined based on a number of times that the query was received, and the plurality of queries can be listed based on the popularity of each of the plurality of queries (e.g., to facilitate display of more popular queries higher in the list of suggested queries).
1. A system for search with autosuggest, comprising:
a processor coupled to a memory having instructions stored thereon that when executed configure the processor to:
determine a plurality of potential query suggestions for a partially entered query string;
merge a plurality of categories associated with a merchant web site into a merged category based on a determination of an aggregate of weights for respective ones of the plurality of categories;
determine a product rank value of a product associated with the merchant web site; and
automatically suggest a plurality of queries corresponding to the merged category based on a query count for each of the queries and indicate a query independent ranked order of the product based on the determined product rank value.
2. The system recited in claim 1 , wherein the query count corresponds to a popularity of the query, and wherein the query count is determined based on a number of times that the query was received.
3. The system recited in claim 1 , wherein the product rank value is determined based on at least one of:
a number of purchases for the product;
a revenue for the product;
a number of times users visited a product's page on the merchant's web site;
a number of page views of the product over a time period on the merchant's web site;
a number of interactions that the product has in a purchase funnel on the merchant's web site;
product review information; and
product return information.
4. The system recited in claim 3 , wherein the interactions that the product has in the purchase funnel comprises at least one of an add-to-cart interaction, a checkout interaction, a quantity purchased interaction, or a sale interaction.
5. The system recited in claim 1 , wherein the processor is further configured to dynamically optimize search results for a merchant web site based on user demand for automatically suggesting the plurality of queries.
6. The system recited in claim 1 , wherein the processor is further configured to determine a number of automatically suggested plurality of queries to return based on a device platform.
7. The system recited in claim 1 , wherein the processor is further configured to provide an option for a user to refine a search query.
8. A method of search with autosuggest, comprising:
determining a plurality of potential query suggestions for a partially entered query string;
merging a plurality of categories associated with a merchant web site into a merged category based on a determination of an aggregate of weights for respective ones of the plurality of categories;
determining a product rank value of a product associated with the merchant web site; and
automatically suggesting a plurality of queries corresponding to the merged category based on a query count for each of the queries and indicating a query independent ranked order of the product based on the determined product rank value.
9. The method recited in claim 8 , wherein the query count corresponds to a popularity of the query, and wherein the query count is determined based on a number of times that the query was received.
10. The method recited in claim 8 , wherein the product rank value is determined based on at least one of:
a number of purchases for the product;
a revenue for the product;
a number of times users visited a product page on the merchant's web site;
a number of page views of the product over a time period on the merchant's web site;
a number of interactions the product has in a purchase funnel on the merchant's web site;
product review information; and
product return information.
11. The method recited in claim 10 , wherein the interactions that the product has in the purchase funnel comprises at least one of an add-to-cart interaction, a checkout interaction, a quantity purchased interaction, or a sale interaction.
12. The method recited in claim 8 , further comprising dynamically optimizing search results for a merchant web site based on user demand for automatically suggesting the plurality of queries.
13. The method recited in claim 8 , further comprising determining a number of automatically suggested plurality of queries to return based on a device platform.
14. The method recited in claim 8 , further comprising providing an option for a user to refine a search query.
15. A non-transitory computer readable storage medium having stored thereon instructions that when executed configure a processor to perform a method of search with autosuggest, the method comprising:
determining a plurality of potential query suggestions for a partially entered query string;
merging a plurality of categories associated with a merchant web site into a merged category based on a determination of an aggregate of weights for respective ones of the plurality of categories;
determining a product rank value of a product associated with the merchant web site; and
automatically suggesting a plurality of queries corresponding to the merged category based on a query count for each of the queries and indicating a query independent ranked order of the product based on the determined product rank value.
16. The non-transitory computer readable storage medium recited in claim 15 , wherein the query count corresponds to a popularity of the query, and wherein the query count is determined based on a number of times that the query was received.
17. The non-transitory computer readable storage medium recited in claim 15 , wherein the product rank value is determined based on at least one of:
a number of purchases for the product;
a revenue for the product;
a number of times users visited a product page on the merchant's web site;
a number of page views of the product over a time period on the merchant's web site;
a number of interactions the product has in a purchase funnel on the merchant's web site;
product review information; and
product return information.
18. The non-transitory computer readable storage medium recited in claim 17 , wherein the interactions that the product has in the purchase funnel comprises at least one of an add-to-cart interaction, a checkout interaction, a quantity purchased interaction, or a sale interaction.
19. The non-transitory computer readable storage medium recited in claim 15 , wherein the method further comprises dynamically optimizing search results for a merchant web site based on user demand for automatically suggesting the plurality of queries.
20. The non-transitory computer readable storage medium recited in claim 15 , wherein the method further comprises:
determining a number of automatically suggested plurality of queries to return based on a device platform; and
providing an option for a user to refine a search query.