Yahoo! Inc.

Particle Media, Inc.

Other

Excalibur Ip, LLC

Beijing Particle Information Technology Co., Ltd.

Zhaohui Zheng

Yi Chang

Gordon Sun

Hongyuan Zha

Anlei Dong

Ciya Liao

Narayanan Sadagopan

Jing Bai

Georges-Eric Albert Marie Robert Dupret

Pranam Kolari

Yoshiyuki Inagaki

Ruiqiang Zhang

Bo Long

Srinivas Vadrevu

Gilbert Leung

Wei Chu

Fernando David Diaz

Jiang Chen

Xuanhui Wang

Karolina Buchner

Ya Zhang

Gilad Avraham Mishne

Keke Chen

Shihao Ji

Zhenzhen Kou

Ling Liu

Taesup Moon

Yuanhua Lv

The present teaching relates to exploring a personal interest space based on a decision tree. In one example, information related to a personal interest space associated with a user is obtained. The personal interest space includes at least one channel of content that the user is interested in. A decision tree created based on information related to a plurality of users with respect to a plurality of channels of content is obtained. Each node of the decision tree is associated with at least one of the plurality of channels of content. A first node of the decision tree is identified based on the information related to the personal interest space associated with the user. A question for the user in connection with the first node of the decision tree is determined. An answer to the question is received from the user. A second node of the decision tree is identified based on the answer. The personal interest space associated with the user is updated based on the at least one channel of content associated with the second node of the decision tree.

Method and system for exploring a personal interest space

The present teaching relates to providing content to a user. Information related to a user is obtained. One or more channels are determined for the user from a plurality of channels based on the information, wherein each of the plurality of channels includes one or more online content items. A personal interest space is established associated with the user based on the information related to the user and the one or more channels. Content is provided in the one or more channels to the user based on information related to the personal interest space.

Method and system for providing organized content

Methods and systems are disclosed that relate to ranking functions for multiple different domains. By way of example but not limitation, ranking functions for multiple different domains may be trained based on inter-domain loss, and such ranking functions may be used to rank search results from multiple different domains so that they may be blended without normalizing relevancy scores.

Methods and systems relating to ranking functions for multiple domains

One particular embodiment clusters a plurality of documents using one or more clustering algorithms to obtain one or more first sets of clusters, wherein: each first set of clusters results from clustering the documents using one of the clustering algorithms; and with respect to each first set of clusters, each of the documents belongs to one of the clusters from the first set of clusters; accesses a search query; identifies a search result in response to the search query, wherein the search result comprises two or more of the documents; and clusters the search result to obtain a second set of clusters, wherein each document of the search result belongs to one of the clusters from the second set of clusters.

Clustering of search results

Performing an on-line recommendation includes: analyzing real-time data from various sources; determining, from the analysis, events in which a user may be interested; extracting the determined events; storing the extracted events in a data store; and performing a recommendation function. The recommendation function includes: ranking the extracted events to determine the events in which the user is most likely to be interested; and performing location-based filtering, retaining those extracted events that are within a geo-location range proximate to the user, thus generating optimal events.

Mobile-based realtime location-sensitive social event engine

In one embodiment, access a set of recency ranking data comprising one or more recency search queries and one or more recency search results, each of the recency search queries being recency-sensitive with respect to a particular time period and being associated with a query timestamp representing the time at which the recency search query is received at a search engine, each of the recency search results being generated by the search engine for one of the recency search queries and comprising one or more recency network resources. Construct a plurality of recency features from the set of recency ranking data. Train a first ranking model via machine learning using at least the recency features.

Incorporating recency in network search using machine learning

An information retrieval system is described herein that monitors a microblog data stream that includes microblog posts to discover and index fresh resources for searching by a search engine. The information retrieval system also uses data from the microblog data stream as well as data obtained from a microblog subscription system to compute novel and effective features for ranking fresh resources which would otherwise have impoverished representations. An embodiment of the present invention advantageously enables a search engine to produce a fresher set of resources and to rank such resources for both relevancy and freshness in a more accurate manner.

Ranking of search results based on microblog data

Methods, systems, and computer programs are presented for providing internet content, such as related news articles. One method includes an operation for defining a plurality of candidates based on a seed. For each candidate, scores are calculated for relevance, novelty, connection clarity, and transition smoothness. The score for connection clarity is based on a relevance score of the intersection between the words in the seed and the words in each of the candidates. Further, the score for transition smoothness measures the interest in reading each candidate when transitioning from the seed to the candidate. For each candidate, a relatedness score is calculated based on the calculated scores for relevance, novelty, connection clarity, and transition smoothness. In addition, at least one of the candidates is selected based on their relatedness scores for presentation to the user.

Learning retrieval functions incorporating query differentiation for information retrieval

A server determines a plurality of immediate candidate items for a first web page to recommend to a user. For each particular immediate candidate item of the plurality of immediate candidate items, the server determines a separate sequence of two or more subsequent possible candidate items for subsequent web pages to recommend to the user in the event that the user selects the particular immediate candidate item. Further, the server selects a particular immediate candidate item from the plurality of immediate candidate items for the first web page to recommend to the user. The first web page that recommends the plurality of immediate candidate items is generated and sent over the Internet to the user.

Value maximizing recommendation systems

Embodiments are directed towards generating market-specific ranking models by leveraging target market specific pairwise preference data. The pairwise preference data includes market-specific training examples, while a ranking model from another market captures the common characteristics of the resulting ranking model. In one embodiment, the ranking model is trained by applying a Tree Based Ranking Function Adaptation (TRADA) algorithm to multi-grade labeled training data, such as editorially generated training data. Then, contradictions between the TRADA generated ranking model and target-market specific pairwise preference data are identified. For each identified contradiction, new training data is generated to correct the contradiction. Then, in one embodiment, an algorithm such as TRADA is applied to the existing ranking model and the new training data to generate a new ranking model.

Cross-market model adaptation with pairwise preference data

Particular embodiments extract a plurality of users, a plurality of establishments, and a plurality of items from dining information provided by at least one of the plurality of users, each of the plurality of establishments sells food or beverage; construct a user-establishment matrix, a user-item matrix, and an establishment-item matrix using the plurality of users, the plurality of establishments, and the plurality of items; generate a user latent representation for the plurality of users, an establishment latent representation for the plurality of establishments, and an item latent representation for the plurality of items; and compute one or more correlations using the user latent representation, the establishment latent representation, or the item latent representation, wherein each of the one or more correlations is between two users, two establishments, two items, one user and one establishment, one of user and one item, or one establishment and one item.

Mobile-based real-time food-and-beverage recommendation system

In one embodiment, access one or more query chains, wherein each one of the query chains comprises two or more search queries, {q, . . . , q}, which are recency-sensitive, are related to the same subject matter, and are issued to a search engine sequentially, and actual click-through information associated with each one of the query chains; and smooth each one of the query chains using the actual click-through information associated with the query chain. To smooth one of the query chains comprises, for each one of search queries, q, in the query chain, where 2&#x2266;j&#x2266;n, if one of the network resources identified for qhas actually been clicked in connection with qby the corresponding one network user, then presume that the one network resource has been clicked in connection with one or more search queries, q, in the query chain, where 1&#x2266;k&#x3c;j.

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Mountain View, CA, United States of America

Zhaohui Zheng