Oath Inc.

Yahoo Assets LLC

Verizon Media Inc.

Dawei Yin

Yi Chang

Jiliang Tang

Jean-Marc Langlois

Changsung Kang

Yuening Hu

Shiyu Chang

Hongbo Deng

Pengyuan Wang

Tim Daly

Hua Ouyang

Yunlong He

Chao Tan

The present teaching relates to recommending content by analyzing the streamed data. A request is received from a user requesting one or more recommendations from a set of items. A first distribution indicative of an interest distribution of the user in a plurality of topics is obtained. For each item, a second distribution indicative of a classification distribution of the item with respect to the plurality of topics is obtained. A score is estimated based on the first distribution and the second distribution, wherein the score indicates likelihood that the user is interested in the item. The scores associated with the set of items are ranked. The one or more recommendations are presented based on the ranked scores.

Method and system for recommending content

The present teaching relates to ranking search content. In one example, a plurality of documents is received to be ranked with respect to a query. Features are extracted from the query and the plurality of documents. The plurality of documents is ranked based on a ranking model and the extracted features. The ranking model is derived to remove one or more documents from the plurality of documents that are less relevant to the query and order remaining documents based on their relevance to the query. The ordered remaining documents are provided as a search result with respect to the query.

Method and system for ranking search content

The present teaching relates to rewriting a query and providing search results. In one example, a plurality of queries is obtained. For each of the plurality of queries, one or more search results are identified. The one or more search results have been obtained in response to the query and have been previously selected by a user submitting the query. A plurality of titles is obtained. Each of the titles corresponds to one of the one or more search results with respect to one of the plurality of queries. A model is generated based on the plurality of queries and the plurality of titles. The model is to be used for rewriting a query.

Method and system for rewriting a query

A location prediction framework is described for applying location labels or tags to target documents and/or identifying location-sensitive queries. Terms in content and queries are represented by corresponding term locations vectors (TLVs) in which the term is represented as a weighted distribution across locations. Each element of a TLV represents a probability that the term corresponding to the TLV relates to a particular location. Predicted locations may be introduced as features to a ranking framework to improve the identification and ranking of search results for a given query.

Location-sensitive ranking for search and related techniques

Predicting locations for web pages and related techniques

Disclosed are systems and methods for improving interactions with and between computers in content searching, generating, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide a unified digital content discovery framework that implements a combination of a logistic loss function and a pair-wise loss function for information retrieval. The logistic loss function reduces non-relevant images from appearing in the retrieved results, while the pair-wise loss function ensures that the highest-quality content is included in such results. The combination of such functions provides a search information retrieval system with the novel functionality of quantifying a search results' relevance and quality in accordance with the searcher's intent.

Computerized system and method for high-quality and high-ranking digital content discovery

Disclosed are systems and methods for improving interactions with and between computers in social media content generation and delivery and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically method for automatically summarizing social media content using a timeline comprising a set (or chain) of episodes and a summary of each episode. The disclosed systems and methods identify a number of episodes based on analysis of each social media content item of a corpus, identify a number of social content items to summarize each episode, and generate a timeline summarization of the corpus of social media content items.

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Dawei Yin