41 results about "Page rank" patented technology

Semantically linked pages are queried based on a user supplied interest vector. The interest vector provides a weight for presenting results from the query of pages. The interest vectors are used to calculate the importance of pages of a query based on the weight of semantic links associated with the page known as page rank indicators. Optionally, the calculation is augmented by other page ranking algorithms. An indication of the resulting pages is displayed according to the calculated importance. Preferably the calculation utilizes a dot product of page rank and user interest vectors.

A modular scoring system using rank aggregation merges search results into an ordered list of results using many different features of documents. The ranking functions of the present system can easily be customized to the needs of a particular corpus or collection of users such as an intranet. Rank aggregation is independent of the underlying score distributions between the different factors, and can be applied to merge any set of ranking functions. Rank aggregation holds the advantage of combining the influence of many different heuristic factors in a robust way to produce high-quality results for queries. The modular scoring system combines factors such as indegree, page ranking, URL length, proximity to the root server of an intranet, etc, to form a single ordering on web pages that closely obeys the individual orderings, but also mediates between the collective wisdom of individual heuristics.

The present invention provides systems and methods of searching web pages relevant to a specific topic based on quality of individual pages. The rank of a page for a keyword may be a combination of analytic rank and editorial rank. The analytic rank of a page may be calculated by combining intrinsic and extrinsic ranks. Intrinsic rank is a measure of relevancy of a page to a given keyword as claimed by an author of the page, while extrinsic rank is a measure of the relevancy of a page on a given keyword as indicated by other pages. The former may be obtained from an analysis of keyword matching in various parts of the page while the latter is obtained from context-sensitive connectivity analysis of the link structure of the entire Internet. Methods are described to solve the self-consistent equation satisfied by the page-weights and site-weights in a very efficient iterative way. The ranking mechanism for multi-word query is also described.

Search term ranking algorithms can be generated and updated based on customer settings, such as where a ranking algorithm is modeled as a combination function of different ranking factors. An end user of a search system provides personalized preferences for weighted attributes, generally or for a single instance of the query. The user also can indicate the relative importance of one or more ranking factors by specifying different weights to the factors. Ranking factors can specify document attributes, such as document title, document body, document page rank, etc. Based on the attribute weights and the received user query, a ranking algorithm function will produce the relevant value for each document corresponding to the user preferences and personalization configurations.

The pages in a network of linked pages are ranked based on the quality of the pages. Page quality is obtained by determining the change over time of the link structure of the page, which is obtained by determining the link structure of the page at different periods of time by taking multiple snapshots of the link structure of the network. The link structures are approximated by their PageRanks, page quality being determined by the formula: $Q\left(p\right)\approx D·\frac{\Delta \mathrm{PR}\left(p\right)}{\mathrm{PR}\left(p\right)}+\mathrm{PR}\left(p\right)$

where Q(p) is the quality of the page, PR(p) is the current PageRank of the page, ΔPR(p) is the change over time in the PageRank of the page, and D is a constant that determines the relative weight of the terms ΔPR(p)/PR(p) and PR(p).

A computer implemented method for scoring a first network node comprising data accessible to a user when connected to the network has steps of (a) determining at least an approximate probability for the first network node that a user not connected to the node will connect to the first node before connecting to any other node; and (b) assigning a score to the node based at least in part on the determined probability.

The present invention provides systems and methods of searching web pages relevant to a specific topic based on quality of individual pages. The rank of a page for a keyword may be a combination of analytic rank and editorial rank. The analytic rank of a page may be calculated by combining intrinsic and extrinsic ranks. Intrinsic rank is a measure of relevancy of a page to a given keyword as claimed by an author of the page, while extrinsic rank is a measure of the relevancy of a page on a given keyword as indicated by other pages. The former may be obtained from an analysis of keyword matching in various parts of the page while the latter is obtained from context-sensitive connectivity analysis of the link structure of the entire Internet. Methods are described to solve the self-consistent equation satisfied by the page-weights and site-weights in a very efficient iterative way. The ranking mechanism for multi-word query is also described.

A page-ranking method includes mining a portion of content of a user workstation which is connectable to a network to detect references to pages of the network. The pages may be ranked based on the detected references.

A processor engine for affecting a website's position on at least one Internet search engine's page ranking comprises at least one processor. The at least one processor is arranged to: load (425) at least one search term into a program (425) running on the at least one processor; load (445) a target list comprising at least one website that is to be promoted; and create (420) an Internet connection. The at least one processor is further arranged to request a search of the at least one search term on the at least one Internet search engine; identify at least one website search hit that matches at least one website on the target list; and access the identified at least one website a plurality of times, affecting the at least one website's position on the Internet search engine's page ranking.

A method for searching Web pages that begins with the identification of query criteria entered into a search provider. A set of Web pages that satisfies the query criteria are determined. Then, a page ranking is ascertained for each Web page in the set. The Web pages are presented in order by page ranking. The page ranking is based upon at least one relevancy factor that includes a browsing-time factor. The browsing-time factor can be calculated from browsing behavior exhibited by users, who provided similar query criteria. The set of users from which the browsing-time factor is calculated can include a current user, a set of users sharing characteristics with the current user, and/or a general set of users. Browsing behavior can include time spent at a Web page, where the browsed Web page is a page that was previously presented as a search result for the similar query criteria.

The page ranking technique described herein employs a Markov Skeleton Mirror Process (MSMP), which is a particular case of Markov Skeleton Processes, to model and calculate page importance scores. Given a web graph and its metadata, the technique builds an MSMP model on the web graph. It first estimates the stationary distribution of a EMC and views it as transition probability. It next computes the mean staying time using the metadata. Finally, it calculates the product of transition probability and mean staying time, which is actually the stationary distribution of MSMP. This is regarded as page importance.

The invention provides cluster page ranking equipment and method based on clustering/classification and time. The cluster page ranking equipment comprises a searcher, a cluster builder, a cluster page ranking calculator, a cluster trend generator and a cluster trend ranking device, wherein the searcher is configured to search relevant documents from data sets according to given query statements and calculate document related values of the searched documents, thus obtaining related document sets of the sequencing; the cluster builder is configured to cluster or classify the related document sets, thus obtaining a cluster; the cluster page ranking calculator based on time is configured to calculate cluster page ranking values (TCP values) based on cluster calculation on the basis of the cluster, and is a combination of document link values based on time of all the documents in the cluster and is used as a combination of the page ranking values based on time, author ranking values based on time and document library ranking values based on time of all the documents in the cluster; the cluster trend generator is configured to calculate the future TCP value of the cluster according to the TCP value; and the cluster trend ranking device is configured to sequence future TCP values, thus obtaining trend.

The invention discloses a friend recommendation method based on a single-source SimRank exact solution. The friend recommendation method comprises the following steps of: converting a target user, users and a relationship among the users into a graph structure G; calculating personalized Page ranks of a source node vi relative to all nodes on the graph, forming a personalized Page ranking vector pi<right arrow>, calculating a no-more-meeting probability of all nodes on the graph structure G, forming a no-more-meeting probability matrix D<^>, calculating the SimRank similarity of the sourcenode vi according to an n-dimensional vector pi<right arrow> and the no-more-meeting probability matrix D<^> to obtain an n-dimensional vector S<right arrow>, repeatedly executing L rounds of calculation of the SimRank similarity, and updating the n-dimensional vector S<right arrow>; and finding out a node corresponding to the t dimension with the maximum value in all dimensions of an n-dimensional vector S<right arrow L>, and recommending the users corresponding to the t nodes to the target user as a result. The friend recommendation method based on the single-source SimRank exact solutioncan guarantee to obtain the exact solution of the single-source SimRank similarity on a large-scale user group within valid time, and the quality and effect of a friend recommendation function are improved.