Hachi (www.gohachi.com) helps you in leveraging your combined network (Facebook, LinkedIn, Gmail, CRM contacts, etc.), to reach out to people who matter to you — be it for professional, social or personal reasons. We do a holistic search across all your social and professional networks, and we optimize the results based on relevance. Like most search sites, you can search using the top search bar, or the advanced search feature where you can search category-wise.

Top search box

Advanced Search

We noticed that very few users used the “advanced search” feature. Google searches have made us lazy, and we expect the website to figure out what are looking for, and search category-wise, and give us the results. Most good sites will give you relevant results, but you will also get a large number of unwanted results. If you search for “Jeff, New York”, you might mean “Jeff who lives in New York (currently, or in past)” , and not the “Jeff who has New York in one of his job descriptions”.

Our goal is to give you the most pertinent results, instead of large number of unwanted results, and to make semantically relevant content appear on top. To achieve this, we implemented a self-improving algorithm which mainly does two things:

Disregarding keyword count while ranking the results Search categorization

Disregarding keyword count while ranking the results

“Searching for people” is different from “searching for articles”, because an individual has properties like location, title, profession, etc. At Hachi, we believe that the “query keyword” count should not be a metric for ranking search results.

We noticed that on LinkedIn, many users hacked their search ranking by including certain keywords multiple times in their profile. Mentioning a keyword like “SEO expert” multiple times in their profile doesn’t make a person a better “SEO expert” than a person who mentioned it only once. Similarly, a person mentioning her company name multiple times in her profile doesn’t make her profile more relevant than the person who mentioned it only once. Both work in the same company, and both should be given equal search ranking in this regard.

We give boolean scores to profile properties. A profile can have a certain property (or not) — and based on that, a score of 0 or 1 is given. If we cannot establish this, the probability of having that property is computed. For instance, a person mentioning “iOS developer” multiple times in her profile is given the same score as a person who mentions it only once. Both get an equal search relevance when “iOS developer” is searched.