Searching for ideograms in an online social network
In one embodiment, a method includes receiving a first query inputted by a first user at a first client system, the first query including one or more n-grams; identifying ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query; calculating, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram; and sending, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms.
1. A method comprising, by one or more computing devices:
receiving, from a first client system of a first user, a first query inputted by the first user at the first client system, the first query comprising one or more n-grams;
identifying one or more ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query;
calculating, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram;
sending, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms;
receiving a second query from a second client system of a second user, the second query inputted by the second user at the second client system, the second query comprising one or more n-grams; and
identifying one or more ideograms, each identified ideogram associated with at least one tag matching at least one of the n-grams of the received second query.
2. The method of claim 1 , wherein at least one of the n-grams corresponds to an emoticon.
3. The method of claim 1 , wherein the first user is a user of an online social network and wherein frequency of use is determined for a plurality of users of the online social network.
4. The method of claim 1 , wherein the frequency of use is determined for the first user.
5. The method of claim 1 , wherein s denotes an ideogram, q denotes the first query, and the use-probability, P(s|q), is computed as:
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[
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6. The method of claim 1 , wherein the first user is a user of an online social network, and wherein the online social network comprises a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising:
a first node corresponding to the first user; and
a plurality of second nodes that each correspond to a second user or a concept associated with the online social network.
7. The method of claim 1 , further comprising determining one or more tags that match at least one of the n-grams of the received first query based on natural-language processing of the received first query.
8. The method of claim 1 , wherein the second user is connected to the first user within a social graph by a threshold degree of separation.
9. The method of claim 1 , further comprising:
ranking each of the identified ideograms based on the respective calculated use-probability; and
determining the first set of ideograms based on the respective rankings associated with the ideograms.
10. The method of claim 9 , wherein the first set of ideograms is presented as an ordered list based on the rankings associated with the ideograms.
11. The method of claim 9 , wherein ranking each of the identified ideograms is further based on a calculated misspelling-probability that at least one of the n-grams of the received first query corresponds to a different n-gram.
12. The method of claim 9 , wherein ranking each of the identified ideograms is further based on one or more attributes of the first user.
13. The method of claim 12 , wherein the one or more attributes comprise age, gender, cultural background, country of birth, current location, languages spoken, occupation, or any combination thereof.
14. The method of claim 1 , wherein one or more of the tags associated with a particular ideogram are dynamically generated using machine learning based on the frequency of use of the ideogram by one or more users of an online social network.
15. The method of claim 1 , further comprising:
receiving a request from the first client system to access one or more restricted ideograms, wherein the first user is not authorized to access the restricted ideograms; and
sending instructions for purchasing access to the one or more restricted ideograms to the first client system for display to the first user.
16. The method of claim 1 , wherein the first set of ideograms is presented on a user interface of a native application associated with an online social network on the first client system.
17. The method of claim 1 , wherein the first set of ideograms is presented on a webpage of an online social network accessed by a browser client of the first client system.
18. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
receive, from a first client system of a first user, a first query inputted by the first user at the first client system, the first query comprising one or more n-grams;
identify one or more ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query;
calculate, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram;
send, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms;
receive a second query from a second client system of a second user, the second query inputted by the second user at the second client system, the second query comprising one or more n-grams; and
identify one or more ideograms, each identified ideogram associated with at least one tag matching at least one of the n-grams of the received second query.
19. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to:
receive, from a first client system of a first user, a first query inputted by the first user at the first client system, the first query comprising one or more n-grams;
identify one or more ideograms, each ideogram being associated with one or more tags, each identified ideogram being associated with at least one tag matching at least one of the n-grams of the received first query;
calculate, for each identified ideogram, a use-probability for the ideogram given the received first query, wherein the use-probability is based at least in part on a frequency of use associated with the ideogram;
send, to the first client system, instructions for presenting a first set of ideograms comprising one or more of the identified ideograms, the first set being determined based on the calculated use-probabilities associated with the ideograms;
receive a second query from a second client system of a second user, the second query inputted by the second user at the second client system, the second query comprising one or more n-grams; and
identify one or more ideograms, each identified ideogram associated with at least one tag matching at least one of the n-grams of the received second query.