40results about How to "Sort results are accurate" patented technology

The embodiment of the invention provides a method and a device for recommending a website, wherein the method comprises the steps of obtaining resources accessed by a user within the current statistical period, determining corresponding type of each resource; respectively counting the sum of access times of each type of corresponding resources; sorting each type of determined resources according to the sum of the access times from large to small; and generating a website recommending page at the previous M types of resources according to the order, wherein M is a positive integer. By adopting the method and device disclosed by the invention, the position and compose type of link information of each website in the recommending page can be dynamically adjusted according to the access behavior of the user; personalized website recommending information can be obtained under the absence of an input or search operation of the user; and the operation of the user is simplified.

The invention discloses a method and a device for sequencing according to contribution degree. The method comprises the following steps: determining sequencing target items and selecting at least two dimensions according to the characteristics of the sequencing target items; calculating sequencing parameters of the sequencing target items on the at least two dimensions; calculating the contribution degree vector of the sequencing target items on the at least two dimensions according to the sequencing parameters; calculating and synthesizing the contribution degree vector for the normalized contribution degree vector according to the weight of the dimensions; and sequencing the sequencing target items according to the synthesized contribution degree vector. According to the method and the device, the contribution degree vector of the sequencing target items on at least two dimensions can be calculated and synthesized, and the sequencing target items are sequenced according to the synthesized contribution degree vector, so the sequencing accuracy degree of the sequencing target items is increased, and the sequence of the sequencing target items accords with the using habits of users.

The invention discloses a fuzzy multi-keyword retrieval method of encrypted data in cloud environment. A file is subjected to set encryption by a data owner to generate a ciphertext file; keywords are extracted from each file; the keywords are subjected to binary segmentation and vectorization to obtain a binary vector group; the binary vector group is subjected to dimensionality reduction and is then inserted into a counting type bloom filter to generate index vectors; each index vector is encrypted to obtain a security index; the ciphertext file and the security indexes are sent to a cloud server; a pre-authorized data user or the data owner extracts the keywords from query data; binary segmentation and vectorization are performed to generate a query vector; encryption is performed to obtain a trap door; the trap door is sent to the cloud server; the cloud server obtains a certain number of files with the highest relevancy degree through query according to the trap door and the security index; after sorting, the files are returned to the data owner. The large data volume of ciphertext multi-keyword retrieval is supported; compared with the prior art, the method has the advantages that the index building and query processes are more efficient; the sorting result is more accurate; the data privacy is effectively protected.

The embodiment of the invention discloses a method for sequencing objects in a heterogeneous network. The method is characterized by comprising the following steps: acquiring a first target type and a second target type; converting the heterogeneous network into a bipartite network constituted by the first target type, the second target type and a path between the first target type and the second target type; converting the bipartite network into a corresponding tensor X; acquiring the reachable probability stationary distribution during random walk in the tensor X; sequencing objects in the first target type, objects in the second target type and the path between the first target type and the second target type according to the reachable probability stationary distribution. Compared with the prior art, the method has the advantage that when the objects in the heterogeneous network are sequenced, not only can the path be used to capture semantic information, but also the semantic information can be fully utilized, so that the sequencing result is relatively accurate.

The embodiment of the invention provides a method and a device for recommending a website, wherein the method comprises the steps of obtaining resources accessed by a user within the current statistical period, determining corresponding type of each resource; respectively counting the sum of access times of each type of corresponding resources; sorting each type of determined resources according to the sum of the access times from large to small; and generating a website recommending page at the previous M types of resources according to the order, wherein M is a positive integer. By adopting the method and device disclosed by the invention, the position and compose type of link information of each website in the recommending page can be dynamically adjusted according to the access behavior of the user; personalized website recommending information can be obtained under the absence of an input or search operation of the user; and the operation of the user is simplified.

A search and sorting method and system for judicial texts, wherein the method includes (1) data preprocessing: collecting judicial text data Doc and legal consulting question Query, and performing word segmentation processing on the collected judicial text data Doc, using the word segmentation Data pre-training of judicial text word vectors; (2) building similarity matrix: using pre-trained word vectors to construct similarity matching matrix M of Query and Doc; (3) intercepting relevant text fragments: according to matching matrix M of Query and Doc Extract local correlation text fragments, splicing multiple local correlation text fragments together to obtain correlation text Ds, and splicing matching matrices of corresponding multiple local correlation text fragments together to obtain matrix Ms; (4) Construct feature vector: calculate Query and the global correlation of the relevant text Ds, construct the feature vector F; (5) Calculate the matching value and sort: input the obtained feature vector F into the neural network model, get the matching score of Query and Doc, according to the size of the matching score put in order.