Yahoo! Inc.

Excalibur Ip, LLC

Yahoo Assets LLC

Oath Inc.

Anlei Dong

Yi Chang

Zhaohui Zheng

Hongbo Deng

Ciya Liao

Georges-Eric Albert Marie Robert Dupret

Shenhong Zhu

Amit Goyal

Narayanan Sadagopan

Lizi Zhang

Nageswara R Pobbathi

Jing Bai

Ruiqiang Zhang

Yoshiyuki Inagaki

Weize Kong

Bo Long

Gilbert Leung

Hui Wu

Jiuhe Gan

Kazuhiro Nakao

Alyssa Glass

Karolina Buchner

Sudarshan Lamkhede

Gilad Avraham Mishne

Pranam Kolari

Ted Eiche

Vignesh Murugesan

Chi-Chung Chan

Shihao Ji

Ling Liu

Methods, systems and programming for providing query suggestions based on user feedback. In one example, a prefix of a query is first received. An input including a prefix of a query is received from a user in a search session. A plurality of query suggestions are fetched based on the prefix of the query. Rankings of the plurality of query suggestions are determined based, at least in part, on the user's previous interactions in the search session with respect to at least one of the plurality of query suggestions. The at least one of the plurality of query suggestions has been previously provided to the user in the search session. The plurality of query suggestions are provided in the search session based on their rankings as a response to the input.

Method and system for providing query suggestions based on user feedback

A system and method is described for large-scale, automated classification of products. The system and method receives information about products, wherein such information includes one or more text metadata fields associated with each product, receives a set of categories, and automatically selects one or more categories from the set of categories to which each product belongs based upon at least one of the one or more text metadata fields associated with each product. A machine learning classifier may be used to automatically select the one or more categories to which each product belongs by operating upon a feature vector for each product derived from text metadata fields of the product description. The machine learning classifier may be trained using a set of pre-categorized product descriptions. The product-category associations generated by the system and method can be used to improve search engine results or product recommendations to consumers.

Automated categorization of products in a merchant catalog

Methods, systems and programming for providing query suggestions based on user's previous search query. In one example, an input including a prefix of a first query is received from a user in a user session. At least a second query that was previously received from the user in the user session is then obtained. A third query is obtained based on the second query and the prefix of the first query. One or more query suggestions are provided to the user as a response to the input. The one or more query suggestions include the third query.

Contextual based search suggestion

Methods, systems and programming for providing query suggestions including entities. In one example, a prefix of a query is first received. A plurality of query suggestions are then identified based on the prefix of the query. The plurality of query suggestions include at least one entity. Scores of each of the plurality of query suggestions are computed using a first model. The first model includes an adjustable parameter used for computing the score of the at least one entity. The plurality of question suggestions are ranked based, at least in part, on the scores.

Method and system for providing query suggestions including entities

Methods, systems and programming for evaluating query suggestions quality. In one example, a plurality of query suggestions are provided in a ranking to a user. A user activity with respect to one of the plurality of query suggestions is detected. A position of the one of the plurality of query suggestions in the ranking is determined. A quality measure of the plurality of query suggestions is calculated based, at least in part, on the user activity and the position of the one of the plurality of query suggestions.

Method and system for evaluating query suggestions quality

Method, apparatus, and programs for customizing a web site are provided. In one example, a method for customizing a web site is provided. One or more representations corresponding to one or more customizable components of a web site are provided. The one or more representations are to be displayed on a display screen to a user. An input entered by the user and directed to a specific customizable component of the web site is received. How to customize the specific customizable component of the web site is determined based on the input. An instruction is generated with respect to the customizable component. The instruction is used to implement customization of the specific customizable component of the web site in accordance with the input from the user.

Method and system for customizing a web site

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

Techniques are described herein for enhancing the ranking products using purchase day based time windows. A purchase day based time window is a time window that is defined to include purchase days selected from a series of consecutive days. A purchase day is a day on which a product associated with the time window is purchased. The series of consecutive days includes the purchase days intermixed with non-purchase day(s). A non-purchase day is a day on which the product associated with the time window is not purchased. The purchase day based time window is further defined to not include the non-purchase day(s).

Ranking products using purchase day based time windows

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

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.

Session based click features for recency ranking

In one embodiment, access one or more query-resource pairs, wherein for each one of the query-resource pairs comprising one of one or more search queries and one of one or more network resources, the one search query is recency-sensitive with respect to a particular time period, and the one network resource is identified for the one search query, and a resource-view count and a resource-click count associated with each one of the query-resource pairs; and construct one or more first click features using the resource-view counts and the resource-click counts associated with the query-resource pairs. To construct one of the first click features in connection with one of the query-resource pairs comprises determine a only-resource-click count associated with the one query-resource pair; and calculate a ratio between the only-resource-click count and the resource-view count associated with the one query-resource pair as the one first click feature.

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Fremont, CA, United States of America

Anlei Dong