Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Clustering Engine that determines that respective actions, performed in a first instance of a virtual environment by a first user during a first time range, correspond with a first latent state. The Clustering Engine determines that respective actions, performed in a second instance of the virtual environment by a second user during the first time range, correspond with a second latent state. The Clustering Engine triggers a first virtual environment feature based on a first latent state parameter space for the first user. The Clustering Engine triggers a second virtual environment feature based on a second latent state parameter space for the second user.

Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a System Tuner for customizing a player's experience. The System Tuner creates an optimal game model based on game-related data of a plurality of players. The optimal game model corresponds to a player segment in the plurality of players. The System Tuner generates one or more rules for building a game variant based on the optimal game model. The System Tuner detects a first player accessing the game. The System Tuner determines a particular player segment to which the player belongs. The System Tuner generates a game variant based on an optimal game model for the particular player segment and sends the game variant to a client device.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to modify a game based on playing style is provided. Player-specific playing style information that indicates one or more attributes of a playing style of a player is accessed. The playing style information is based on gameplay behavior of the player. Game parameters that defines at least some aspects of a game level forming part of the game is accessed. In an automated operation one or more of the game parameters are modified, thereby a difficulty of the game level for the player based on the one or more playing style attributes is modified. The modified game parameters are caused to be presented on a client device associated with the player of the game level.

Abstract: A system, a non-transitory machine-readable storage medium storing instructions, and a computer-implemented method to adjust twitch response and collision determination to a player's skill level is provided. Gameplay information of a computer-implemented game is accessed. The gameplay information including input information indicating one or more attributes of gameplay input provided by the player. A gameplay skill level of the player of the player is estimated based on the one or more gameplay input attributes. Based on the estimated gameplay skill level of the player, respective values of one or more gameplay parameters that determine on-screen behavior of virtual in-game objects are modified, thereby a gameplay difficulty level is modified.

Abstract: A structured query may specify attribute values for attributes. An estimate of the number of items that will match the structured query if it is applied to a structured database is determined. If the estimated number of items is below a threshold, the structured query may be relaxed to form new candidate structured queries. The number of candidate queries may be determined based on a desired running time. Each of the candidate structured queries may be determined by changing one or more attribute values of the attributes of the structured query. Estimates of the number of items each of the candidate structured queries will match is determined, and the candidate structured query that has the highest matching estimation is used to query the database. The matching results may be output.

Abstract: User generated reviews and scores associated with the reviews may be used to train a review scoring model with textual features of the reviews. The review scoring model may be used to predict scores for newly received reviews. One or more constraints based on social networking application data associated with an author of a review may be used to adjust the predicted score of the review.

Abstract: Described is answering online web queries from a structured data store, such as a products database. An online web search query is modified into an evaluation expression for accessing a structured data store (e.g., a database, XML file, a flat table) to find search results. A query is matched to a pattern, which is used to generate the expression, as well as determine to which structured data store the query is routed. Tokens (e.g., words) in the query are processed against a dictionary of token classes (sets of tokens) and patterns (sets of token classes) to annotate the query to map the query to the matching pattern. A translation process generates the expression based on translation hints that correspond to the matching pattern. Also described is offline mining of query logs to generate the dictionaries.

Abstract: Described is releasing output data representing a search log, in which the data is suitable for most data mining/analysis applications, but is safe to publish by preserving user privacy. The search log is processed such that a query is only included if a sufficient count of that query is present; noise may be added. User contributions that are considered may be limited to a maximum number of queries. The output may indicate how often (possibly plus noise) that each query appeared. Other output may comprise a query-action graph, a query-inaction graph and/or a query-reformulation graph, with nodes representing queries and nodes representing actions, inactions or reformulations (e.g., clicked URLs, skipped URLs, or selected related queries), and edges between nodes representing action, skip or selection counts (possibly plus noise). The output may correspond to the top results/related queries returned from a search.

Abstract: Documents are replicated among servers comprising a search engine based on the value of each document by approximating its value as one of the top search results for one or more exemplary queries. Documents are allocated among servers comprising a search engine by calculating a relevance value for each document and then distributing the documents evenly to the servers. A subset of servers are selected from among a plurality of servers comprising a search engine using term-based, server-specific histograms reflecting the number of instances of the term in each document allocated to each server, and then selecting servers to service a query based on the documents on those servers.

Abstract: Compression of extensive, rule-based grammars used to facilitate search queries is provided herein. Rule-based grammars include a list of rules that each comprise a sequence of token classes. Each token class is a logical grouping of tokens, and each token is a string of characters. A grammar is parsed to identify rules and token classes. Unimportant token classes are identified and sets of unimportant token classes are merged to generated merged token classes. A compressed grammar is generated by substituting the merged token classes into the grammar for corresponding unimportant token classes used to generate the merged token classes.

Abstract: Techniques are provided for selecting a limited but comprehensive set of high-quality users reviews covering several different aspects or attributes of a reviewed item. For several implementations, selection methodologies approach the challenge as a maximum coverage problem and provide a generic formalism to model the different variants of the review-set selection. Variations to such implementation may also employ different algorithms in consideration of different variants and weightings of those variants. Select implementations employ methodologies that collectively consider attributes of the item discussed in the reviews, the quality of the reviews themselves, and the viewpoint of the reviews (e.g., positive or negative) as input values in order to provide outputs that cover as many attributes of the item as possible, comprising high quality reviews representing different viewpoints.

Abstract: Documents are replicated among servers comprising a search engine based on the value of each document by approximating its value as one of the top search results for one or more exemplary queries. Documents are allocated among servers comprising a search engine by calculating a relevance value for each document and then distributing the documents evenly to the servers. A subset of servers are selected from among a plurality of servers comprising a search engine using term-based, server-specific histograms reflecting the number of instances of the term in each document allocated to each server, and then selecting servers to service a query based on the documents on those servers.

Abstract: Compression of extensive, rule-based grammars used to facilitate search queries is provided herein. Rule-based grammars include a list of rules that each comprise a sequence of token classes. Each token class is a logical grouping of tokens, and each token is a string of characters. A grammar is parsed to identify rules and token classes. Unimportant token classes are identified and sets of unimportant token classes are merged to generated merged token classes. A compressed grammar is generated by substituting the merged token classes into the grammar for corresponding unimportant token classes used to generate the merged token classes.

Abstract: A structured query may specify attribute values for attributes. An estimate of the number of items that will match the structured query if it is applied to a structured database is determined. If the estimated number of items is below a threshold, the structured query may be relaxed to form new candidate structured queries. The number of candidate queries may be determined based on a desired running time. Each of the candidate structured queries may be determined by changing one or more attribute values of the attributes of the structured query. Estimates of the number of items each of the candidate structured queries will match is determined, and the candidate structured query that has the highest matching estimation is used to query the database. The matching results may be output.

Abstract: Compression of extensive, rule-based grammars used to facilitate search queries is provided herein. Rule-based grammars includes a list of rules that each comprise a sequence of token classes. Each token class is a logical grouping of tokens, and each token is a string of characters. A grammar is parsed to identify rules and token classes. Unimportant token classes are identified and sets of unimportant token classes are merged to generated merged token classes. A compressed grammar is generated by substituting the merged token classes into the grammar for corresponding unimportant token classes used to generate the merged token classes.

Abstract: User generated reviews and scores associated with the reviews may be used to train a review scoring model with textual features of the reviews. The review scoring model may be used to predict scores for newly received reviews. One or more constraints based on social networking application data associated with an author of a review may be used to adjust the predicted score of the review.

Abstract: Evaluating content includes receiving content, analyzing the content for web spam using a content-based identification technique, and classifying the content according to the analysis. An index of analyzed contents may be created. A system for evaluating content includes a storage device configured to store data and a processor configured to analyze content for web spam using content-based identification techniques.

Abstract: Described is answering online web queries from a structured data store, such as a products database. An online web search query is modified into an evaluation expression for accessing a structured data store (e.g., a database, XML file, a flat table) to find search results. A query is matched to a pattern, which is used to generate the expression, as well as determine to which structured data store the query is routed. Tokens (e.g., words) in the query are processed against a dictionary of token classes (sets of tokens) and patterns (sets of token classes) to annotate the query to map the query to the matching pattern. A translation process generates the expression based on translation hints that correspond to the matching pattern. Also described is offline mining of query logs to generate the dictionaries.

Abstract: Described is releasing output data representing a search log, in which the data is suitable for most data mining/analysis applications, but is safe to publish by preserving user privacy. The search log is processed such that a query is only included if a sufficient count of that query is present; noise may be added. User contributions that are considered may be limited to a maximum number of queries. The output may indicate how often (possibly plus noise) that each query appeared. Other output may comprise a query-action graph, a query-inaction graph and/or a query-reformulation graph, with nodes representing queries and nodes representing actions, inactions or reformulations (e.g., clicked URLs, skipped URLs, or selected related queries), and edges between nodes representing action, skip or selection counts (possibly plus noise). The output may correspond to the top results/related queries returned from a search.

Abstract: Described is using modifiers in online search queries for queries that map to a database table. A modifier (e.g., an adjective or a preposition) specifies the intended meaning of a target, in which the target maps to a column in that table. The modifier thus corresponds to one or more functions that determine which rows of data in the column match the query, e.g., “cameras under $400” maps to a camera (or product) table, and “under” is the modifier that represents a function (less than) that is used to evaluate a “price” target/data column. Also described are different classes of modifiers, and generating the dictionaries for a domain (corresponding to a table) via query log mining.