Abstract: In an example embodiment, an item characteristic is received, the item characteristic pertaining to an item being listed for sale, by a seller, via an ecommerce service. Then, a plurality of past transactions of items having the item characteristic are analyzed. Based on this analysis, a first set of one or more optimal listing configuration parameters are identified in accordance with a first set of listing criteria. Then, the first set of one or more identified optimal listing configuration parameters to the seller in a user interface that permits the seller to change one or more listing configuration parameters based on the presentation.

Abstract: In an example embodiment, an item characteristic is received, the item characteristic pertaining to an item being listed for sale, by a seller, via an ecommerce service. Then, a plurality of past transactions of items having the item characteristic are analyzed. Based on this analysis, a first set of one or more optimal listing configuration parameters are identified in accordance with a first set of listing criteria. Then, the first set of one or more identified optimal listing configuration parameters to the seller in a user interface that permits the seller to change one or more listing configuration parameters based on the presentation.

Abstract: In an example embodiment, an item characteristic is received, the item characteristic pertaining to an item being listed for sale, by a seller, via an ecommerce service. Then, a plurality of past transactions of items having the item characteristic are analyzed. Based on this analysis, a first set of one or more optimal listing configuration parameters are identified in accordance with a first set of listing criteria. Then, the first set of one or more identified optimal listing configuration parameters to the seller in a user interface that permits the seller to change one or more listing configuration parameters based on the presentation.

Abstract: Redirection (“biasing”) architecture that automates the selection of topical terms in a search query, and provides a user-selectable (e.g., clickable) interface which enables the user to quickly and easily re-formulate and execute a new query using terms that return more focused search results. The redirection of search by biasing the terms (strings) can also be performed by indicating that certain results are not interesting to the user. For example, one way using an existing search engine, is to apply a search operator (e.g., a hyphen) to indicate that certain terms must not occur in the search results (negation). Accordingly, by automatically selecting topical terms to negate, constructing a query, and embedding the negation in a link with each results page result, considerable manual and cognitive effort is saved.

Abstract: Computer-readable media, computer systems, and computing methods are provided for classifying search results as either of good quality or of poor quality. Initially, a portion of the search results, such as the highest ranked documents, are selected for evaluation. A level of quality for each of the selected search results is determined using a classification process that includes the following steps: targeting features demonstrated by the selected search results to be evaluated; evaluating the selected features to generate a level-of-quality score for each of the selected search results; comparing the score against a predefined threshold value; and, based on the comparison, assigning each of the selected search results an absolute measurement. The absolute measurement indicates poor quality when the score is less than the threshold value. Upon recognizing that the selected search results are of poor quality, automatically executing a corrective action that reformulates the issued search query.

Abstract: In an example embodiment, an item characteristic is received, the item characteristic pertaining to an item being listed for sale, by a seller, via an ecommerce service. Then, a plurality of past transactions of items having the item characteristic are analyzed. Based on this analysis, a first set of one or more optimal listing configuration parameters are identified in accordance with a first set of listing criteria. Then, the first set of one or more identified optimal listing configuration parameters to the seller in a user interface that permits the seller to change one or more listing configuration parameters based on the presentation.

Abstract: Redirection (“biasing”) architecture that automates the selection of topical terms in a search query, and provides a user-selectable (e.g., clickable) interface which enables the user to quickly and easily re-formulate and execute a new query using terms that return more focused search results. The redirection of search by biasing the terms (strings) can also be performed by indicating that certain results are not interesting to the user. For example, one way using an existing search engine, is to apply a search operator (e.g., a hyphen) to indicate that certain terms must not occur in the search results (negation). Accordingly, by automatically selecting topical terms to negate, constructing a query, and embedding the negation in a link with each results page result, considerable manual and cognitive effort is saved.

Abstract: Computer-readable media, computer systems, and computing devices method of facilitating presentation of alternative query suggestions. The method includes referencing an initial query having a plurality of terms. A term(s) to remove from the initial query is identified, for example, based at least on a number of times the term(s) occur in previous queries. The initial query is modified by removing the term(s) from the initial query. Thereafter, the modified query is used to identify an alternative query suggestion(s) that includes at least one term that matches at least one term of the modified query.

Abstract: Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate reformulating user queries in association with a search box are provided. A user query having a plurality of terms is received and a determination is made that the received user query satisfies a threshold. Based on the received user query, a first set of reformulated user queries is determined. The first set of reformulated user queries includes a plurality of member queries. The plurality of member queries may include one or more suggested query term alterations and/or one or more suggested query term deletions. The member queries may be categorized into groups and/or ranked prior to presentation to a user. A selection option may also be presented for a user to input additional query terms.

Abstract: Computer-readable media, computer systems, and computing methods are provided for classifying search results as either of good quality or of poor quality. Initially, a portion of the search results, such as the highest ranked documents, are selected for evaluation. A level of quality for each of the selected search results is determined using a classification process that includes the following steps: targeting features demonstrated by the selected search results to be evaluated; evaluating the selected features to generate a level-of-quality score for each of the selected search results; comparing the score against a predefined threshold value; and, based on the comparison, assigning each of the selected search results an absolute measurement. The absolute measurement indicates poor quality when the score is less than the threshold value. Upon recognizing that the selected search results are of poor quality, a corrective action that reformulates the issued search query is automatically invoked.

Abstract: Computer-readable media, computer systems, and computing methods are provided for classifying search results as either of good quality or of poor quality. Initially, a portion of the search results, such as the highest ranked documents, are selected for evaluation. A level of quality for each of the selected search results is determined using a classification process that includes the following steps: targeting features demonstrated by the selected search results to be evaluated; evaluating the selected features to generate a level-of-quality score for each of the selected search results; comparing the score against a predefined threshold value; and, based on the comparison, assigning each of the selected search results an absolute measurement. The absolute measurement indicates poor quality when the score is less than the threshold value. Upon recognizing that the selected search results are of poor quality, automatically executing a corrective action that reformulates the issued search query.

Abstract: A familiarity level classifier comprises a stopwords engine for conducting a stopwords analysis of stopwords, e.g., introductory level stopwords and advanced level stopwords, in a document, e.g., a website; and a familiarity level classifier module for generating a document familiarity level based on the stopwords analysis. The classifier may be in an indexing module, a search engine, a user computer, or elsewhere in a computer network. The classifier may also include a reading level engine for conducting a reading level analysis of the document, and wherein the familiarity level classifier module is configured to generate the familiarity level also based on the reading level analysis. The classifier may also include a document features engine for conducting a feature analysis of the document, and wherein the familiarity level classifier module is configured to generate the document familiarity level also based on the feature analysis.

Abstract: A familiarity level classifier comprises a stopwords engine for conducting a stopwords analysis of stopwords, e.g., introductory level stopwords and advanced level stopwords, in a document, e.g., a website; and a familiarity level classifier module for generating a document familiarity level based on the stopwords analysis. The classifier may be in an indexing module, a search engine, a user computer, or elsewhere in a computer network. The classifier may also include a reading level engine for conducting a reading level analysis of the document, and wherein the familiarity level classifier module is configured to generate the familiarity level also based on the reading level analysis. The classifier may also include a document features engine for conducting a feature analysis of the document, and wherein the familiarity level classifier module is configured to generate the document familiarity level also based on the feature analysis.