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An important subset of personal context, social context refers to the people one knows and cares about. The“Jose Gonzales” with whom you have 5 mutual friends is a better result than those with no friends in common. Note that the better job search does at helping you find and connect, the better your search results will be going forward. While personal context makes use of things you care about, social context deals with the things your friends care about. Since calculating social context for every query is technically complex, we built a separate service for it. We will cover the details of this service in a future blog post.
The Query: We tokenize the query based on the suspected language (Chinese tokenized on characters, English on spaces), correct potential spelling mistakes, find "Elizabeth Jones" even though you typed in "Liz Jones," etc. We also prioritize results based on how they matched the query; e.g we rank entities with "chicago" in their title differently from those located in Chicago. We've made good progress in understanding queries, but have a lot more left to do.
Global Popularity: An entity popular amongst a large audience deserves high ranking. Someone searching “Michael Jackson” is more likely to want the pop star than a friend of a friend by the same name. To determine global popularity we look at how many people are connected to an entity as well as how engaged they are -- a Poker application with a few frequent users might be more relevant than one with several infrequent users.
Complexities of User-Centric Search
Our emphasis on personal and social context leads to some interesting technical challenges which make it different from the traditional search problem.
Ranking on the critical path: Since our most important ranking features depend on who the searcher is, all our feature generation and ranking happens as a part of the query execution workflow i.e. our indices can't store pre-ranked results to optimize lookups. . Instead, we have to generate ranking features like is_same_high_school and num_mutual_connections on the fly for every potential result, and run them through our ranking model to find the best results. Making this model better and faster is a major focus for the team this year.
No query cache: Caching allows a service to compute results once and reuse them across multiple requests. Usually a small number of unique queries make up a large portion of all requests (see Zipf's Law), so most search engines can cache the best results for their most popular queries. Good caching strategies can give you a 50-60% cache hit-rate - at a large scale, this means millions of dollars of savings and much improved performance.
Large hot index: Another way search engines usually reduce work is to create a much smaller ‘hot’ index comprised of high quality documents. Enough results from the hot index means never having to hit the slower cold index. This works when the hot index contains the set of documents that have a high likelihood of being the best or ‘good enough’ for most queries. Unfortunately, there is no such thing as good-enough when you’re looking for a specific person on Facebook, rendering most of our index 'hot.’
The Query: We tokenize the query based on the suspected language (Chinese tokenized on characters, English on spaces), correct potential spelling mistakes, find "Elizabeth Jones" even though you typed in "Liz Jones," etc. We also prioritize results based on how they matched the query; e.g we rank entities with "chicago" in their title differently from those located in Chicago. We've made good progress in understanding queries, but have a lot more left to do.
Global Popularity: An entity popular amongst a large audience deserves high ranking. Someone searching “Michael Jackson” is more likely to want the pop star than a friend of a friend by the same name. To determine global popularity we look at how many people are connected to an entity as well as how engaged they are -- a Poker application with a few frequent users might be more relevant than one with several infrequent users.
Complexities of User-Centric Search
Our emphasis on personal and social context leads to some interesting technical challenges which make it different from the traditional search problem.
Ranking on the critical path: Since our most important ranking features depend on who the searcher is, all our feature generation and ranking happens as a part of the query execution workflow i.e. our indices can't store pre-ranked results to optimize lookups. . Instead, we have to generate ranking features like is_same_high_school and num_mutual_connections on the fly for every potential result, and run them through our ranking model to find the best results. Making this model better and faster is a major focus for the team this year.
No query cache: Caching allows a service to compute results once and reuse them across multiple requests. Usually a small number of unique queries make up a large portion of all requests (see Zipf's Law), so most search engines can cache the best results for their most popular queries. Good caching strategies can give you a 50-60% cache hit-rate - at a large scale, this means millions of dollars of savings and much improved performance.
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Large hot index: Another way search engines usually reduce work is to create a much smaller ‘hot’ index comprised of high quality documents. Enough results from the hot index means never having to hit the slower cold index. This works when the hot index contains the set of documents that have a high likelihood of being the best or ‘good enough’ for most queries. Unfortunately, there is no such thing as good-enough when you’re looking for a specific person on Facebook, rendering most of our index 'hot.’
