Airbnb, Inc.

Facebook, Inc.

Kedar Bellare

Soumyadip Banerjee

Tyler Sax

Min Liu

Karen Ouyang

Thomas Jean Marc Legrand

Ling Bao

Bosmat Mazal Eldar

Adam James Shutsa

Christina Joan Sauper

Malay Haldar

Shuoyuan Lin

Shanni Weilert

Clarence Chin-wei Quah

Yonghua Xu

Vivek Bhardwaj

Surbhi Sethi

Ang Li

Devansh Gupta

Quang Xuan Do

Xiaotang Wang

Sherry Therese Chen

Chirag Mahapatra

Hongwei Zhang

Hongru Hou

A flexible listings search system can receive and return results for flexible listing searches. For example, the system can perform micro-flexible searches (e.g., plus or minus a few days) or super flexible searches (e.g., a time span in one or more months), using listing arrays that can be rapidly accessed to efficiently identify and return results. The search system can further perform flexible destination searches for different categories of accommodations for display in a viewport (e.g., map bounding box). The system can further perform fuzzy searches to identify and return broader results for flexible queries.

Flexible listings searches

A system that enables searching for representing listings for accommodation reservations on a map is described. The system receives, by a network site of a listing network platform, input comprising search criteria associated with a geographical region and identifies a plurality of listings matching the search criteria. The system identifies a first subset listings of the plurality of listings that is each associated with a respective location that is within a boundary associated with the geographical region and a second subset listings of the plurality of listings that is each associated with a respective location that is outside the boundary associated with the geographical region. The system visually distinguishes the first subset of listings from the second subset of listings on a map-based graphical user interface (GUI) that represents the plurality of listings matching the search criteria.

Representing listings for reservations on map

Systems and methods are provided for generating a search query based on received user data to perform an internet search using the search query. The systems and methods further extract data from internet search results from the internet search using the search query, generate internet search income records from the extracted data, generate income records from one or more databases comprising income records, and combine the internet search income records and the generated income records from the one or more databases to form combined income record results. The systems and methods further identify candidate income records from the combined income record results, extract features from each candidate income record for generating an income prediction, and generate the income prediction using a machine learning model to predict an income, based on the extracted features from the candidate income records.

Verification model using neural networks

In one embodiment, a method includes accessing posts of an online social network, each post being associated with a first entity of the online social network, classifying, based on content and metadata associated with each post, one or more of the posts as being relevant to the first entity, extracting a set of one or more n-grams from the content of the posts classified as being relevant to the first entity, filtering the set of n-grams to remove one or more of the extracted n-grams from the set of n-grams, calculating a quality score for each n-gram in the filtered set of n-grams, generating a snippet-module including one or more of the extracted n-grams from the filtered set of n-grams having quality-scores greater than a threshold quality-score, and sending, to a client system of a first user of the online social network, the snippet-module for display to the user.

Generating snippet modules on online social networks

Growing community of inventors

San Jose, CA, United States of America

Kedar Bellare