A method for recommending knowledge map based on location service domain

Abstract

The invention discloses a recommending method of a knowledge map based on the location service field, which includes: extracting a location entity and obtaining an entity set as a seed set of the knowledge map, corresponding the seed set with the entities in the knowledge map, forming an entity correspondence table, in a knowledge map triple in that knowledge map, using Word2Vec model to embed vocabulary into n-dimensional space, generating corresponding vectors, obtaining a position or domain entity vector set E and a relation vector set R, translating the entity vector set E and the relationvector set R by using a TransE algorithm, and obtaining a triple vector set capable of quickly calculating semantic similarity between entities; according to the location or domain entity vector setE, calculating respectively the semantic similarity simA, B (A, B) between the searching locations or domains to generate the semantic similarity matrix of the tourism location, using Semantic Similarity Matrix for Top-K Recommendation List, clustering the recommendation list according to machine learning clustering algorithm, and recommending the clustering result to the user. The method has highprecision and can solve the problems of cold start and sparsity.

Description

technical field [0001] The invention relates to the technical field of recommendation algorithms, in particular to a recommendation method based on a knowledge map in the field of location services. Background technique [0002] With the improvement of people's living standards, according to statistics, there are billions of people traveling every year, and many people do not have a good travel goal. In order to achieve this goal, it is necessary to have a precise location or type of user interest. Recommendations, while the existing recommendation systems only use the interaction information of users and location popularity or ratings as input, which will bring two problems: First, in actual scenarios, the interaction information of users and location popularity is often very single , and the ratings are often sparse. For example, a user may like a city with more natural scenery, but the current location popularity is a city that is biased towards cultural ancient cities, w...

AI Technical Summary

Problems solved by technology

[0002] With the improvement of people's living standards, according to statistics, there are billions of people traveling every year, and many people do not have a good travel goal. In order to achieve this goal, it is necessary to have a precise location or type of user interest. Recommendations, while the existing recommendation systems only use the interaction information of users and location popularity or ratings as input, which will bring two problems: first, in actual scenarios, the interaction information of users and location popularity is often very single , and the ratings are often sparse. For example, a user may like a city with more natural scenery, but the current location popularity is a city that is biased towards cultural ancient cities, which makes users often need to search for a long time to find a location that matches their travel interests or may be Reduce the user's willingness to travel, and if an APP has tens of thousands of users, only a small number of users will seriously score, resulting in sparseness, which will greatly increase the risk of overfitting of the algorithm; second, for new users, Because the system does not have its historical interaction information, it cannot be accurately modeled and recommended, which is also called the cold start problem (coldstart problem)

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