CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a U.S. National Stage of International Application No. PCT/CN2017/119765, filed on Dec. 29, 2017, designating the United States, which claims priority to Chinese Patent Applications No. 201710596879.4, entitled “INFORMATION SEARCH METHOD, APPARATUS, AND SYSTEM” and filed on Jul. 20, 2017, both of which are incorporated by reference in their entireties.

TECHNICAL FIELD

The present disclosure relates to the field of information search technology, and specifically, to an information search method, apparatus, and system.

BACKGROUND

At present, with the fast development of Internet of Things (IoT) technology, an increasingly large number of people can find and learn about offline scenic spots, merchants, and the like on the internet. A user uses a terminal such as a computer or a mobile phone to find offline scenic spots, merchants, and the like through an Online To Offline (O2O, an IoT concept that combines offline business opportunities and the internet) search engine to learn about scenic spots, promotion activities of merchants, and comments of previous customers or use online services such as booking a ticket, placing an order or making a payment.

Currently, O2O search engines provided in related technologies perform search mainly based on local information. For example, a user searches for a national hotel chain and can only obtain local search results. That is, only related search results in a current positioning city can be provided to the user.

However, during actual application, a user usually has many non-local search requirements. For example, before making a trip, the user usually needs to search for scenic spot information and hotel information of a destination city to, for example, book a ticket or a hotel. Existing O2O search engines usually cannot satisfy requirements of users. For example, when a user searches “Huashan” in Shanghai, obtained results are all about “Huashan Hospital”. If the user intends to search for the scenic spot “Huashan”, the user needs to manually switch the city, resulting in complex operations and relatively low efficiency.

Therefore, there is currently an urgent need for a technical solution that can satisfy non-local search requirements of users, thereby improving the efficiency of non-local search of users and improving user experience.

SUMMARY

In view of this, an objective of embodiments of the present disclosure is to provide an information search method, apparatus, and system.

According to a first aspect, an embodiment of the present disclosure provides an information search method applied to a server. The method includes: receiving a search word sent by a user by using a client; recognizing search intention of the user according to feature information of the search word and/or search context feature information; performing search by using a search policy corresponding to the recognized search intention and according to the search word to acquire information search results associated with the search word, where the information search results include a local search result and/or a non-local search result; and sending the information search results to the client, so that the client displays the information search results.

Optionally, recognizing search intention of the user according to feature information of the search word and/or search context feature information includes: acquiring the feature information of the search word and/or the search context feature information; determining non-local search preference of the user according to the feature information of the search word and/or the search context feature information and by using a pre-trained non-local search preference determination model; and determining the search intention of the user according to the non-local search preference and a preset preference threshold condition.

Optionally, the non-local search preference determination model is trained in the following manner including: constructing the non-local search preference determination model by using a feature variable of the search word and/or a search context feature variable as an independent variable and using a non-local search preference feature as a dependent variable; acquiring training samples generated according to a plurality of search click historical records; and training the non-local search preference determination model by using the training samples.

Optionally, constructing the non-local search preference determination model includes: constructing the non-local search preference determination model by using a logic regression model.

Optionally, constructing the non-local search preference determination model by using a logic regression model specifically includes: constructing the non-local search preference determination model by using the following Formula 1:
remoteprob=sigmoid(w1x1+w2x2+ . . . +wnxn),  Formula 1:

in the Formula 1, remoteprobrepresents the non-local search preference feature, xnrepresents an eigenvalue of an n-th feature variable, wnrepresents a weight corresponding to the n-th feature variable, a weight corresponding to each feature variable is determined by training according to chosen training samples, the function sigmoid is a threshold function of a neural network, and the threshold function is used to map a variable into a value between 0 and 1.

Optionally, before the recognizing search intention of the user according to feature information of the search word and/or search context feature information, the method further includes: matching the search word against a preset local and non-local entity word library, to determine whether the search word has a possibility of non-local search; and if there is a possibility of non-local search, performing the step of recognizing search intention of the user according to feature information of the search word and/or search context feature information.

Optionally, performing search by using a search policy corresponding to the search intention and according to the search word to acquire information search results associated with the search word includes: if the search intention is that the user has both local search intention and non-local search intention, performing local search and non-local search according to the search word, to obtain the information search results including a plurality of local search results and a plurality of non-local search results.

Optionally, before sending the information search results to the client, the method further includes: incorporating the plurality of local search results into a local search result display region; incorporating the plurality of non-local search results into a non-local search result display region; determining a display order of the local search result display region and the non-local search result display region according to the non-local search preference of the user and/or quality of the local and non-local search results; and generating the information search results including the local search result display region and the non-local search result display region in the display order.

Optionally, the local search result display region is a collapsible display region, and after incorporating the plurality of local search results into a local search result display region, the method further includes: determining an order (sequence) of the plurality of local search results in the local search result display region according to pre-estimated click through rates of the local search results, and determining a quantity of local search results displayed when the local search result display region is in a collapsed state.

Optionally, in a case that the non-local search result display region is a collapsible display region, after incorporating the plurality of non-local search results into a non-local search result display region, the method may further include: determining an order of the plurality of non-local search results in the non-local search result display region according to a pre-estimated click through rate of each of the plurality of the non-local search results, and determining a quantity of non-local search results displayed when the non-local search result display region is in a collapsed state.

Optionally, the information search method may further include: acquiring a click operation log of the user on the information search results; and performing iterative optimization on the non-local search preference determination model according to the click operation log.

According to a second aspect, an embodiment of the present disclosure further provides an information search method applied to a client. The method includes: monitoring a search word entered by a user; sending the search word to a server; receiving information search results fed back by the server, where the information search results are obtained by the server through search by using a search policy corresponding to search intention of the user recognized according to feature information of the search word and/or search context feature information, and the information search results include a local search result and/or a non-local search result; and displaying the information search results.

Optionally, the information search results may include a local search result display region, a non-local search result display region, and a display order. The local search result display region may include one or more local search results, and the non-local search result display region may include one or more non-local search results. Correspondingly, the displaying the information search results may include: displaying the local search result display region and the non-local search result display region on the same screen of the client in the display order.

According to a third aspect, an embodiment of the present disclosure further provides an information search apparatus applied to a server. The apparatus includes: a search word receiving module configured to receive a search word sent by a user by using a client; a search intention recognition module configured to recognize search intention of the user according to feature information of the search word and/or search context feature information; a policy search module configured to perform search by using a search policy corresponding to the search intention and according to the search word to acquire information search results associated with the search word, where the information search results include a local search result and/or a non-local search result; and a search result sending module configured to send the information search results to the client, so that the client displays the information search results.

Optionally, the search intention recognition module is specifically configured to: acquire the feature information of the search word and/or the search context feature information; determine non-local search preference of the user according to the feature information of the search word and/or the search context feature information and by using a pre-trained non-local search preference determination model; and determine the search intention of the user according to the non-local search preference and a preset preference threshold condition.

Optionally, the non-local search preference determination model is trained in the following manner including: constructing the non-local search preference determination model by using a feature variable of the search word and/or a search context feature variable as an independent variable and using a non-local search preference feature as a dependent variable; acquiring training samples generated according to a plurality of search click historical records; and training the non-local search preference determination model by using the training samples.

Optionally, the non-local search preference determination model is constructed by using a logic regression model.

Optionally, the non-local search preference determination model is specifically constructed by using the following Formula 1:
remoteprob=sigmoid(w1x1+w2x2+ . . . +wnxn),  Formula 1:

in the formula, remoteprobrepresents the non-local search preference feature, xnrepresents an eigenvalue of an n-th feature variable, wnrepresents a weight corresponding to the n-th feature variable, a weight corresponding to each feature variable is determined by training according to chosen training samples, the function sigmoid is a threshold function of a neural network, and the threshold function is used to map a variable into a value between 0 and 1.

Optionally, the information search apparatus further includes an entity word matching module configured to: match the search word against a preset local and non-local entity word library, to determine whether the search word has a possibility of non-local search, and if there is a possibility of non-local search, perform the step of recognizing search intention of the user according to feature information of the search word and/or search context feature information.

Optionally, the policy search module is specifically configured to: if the search intention is that the user has both local search intention and non-local search intention, perform local search and non-local search according to the search word, to obtain the information search results including a plurality of local search results and a plurality of non-local search results.

Optionally, the information search apparatus further includes: a result incorporation module configured to: incorporate the plurality of local search results into a local search result display region, and incorporate the plurality of non-local search results into a non-local search result display region; a display order determining module, configured to determine a display order of the local search result display region and the non-local search result display region according to the non-local search preference of the user and/or quality of the local and non-local search results; and a search result generation module, configured to generate the information search results including the local search result display region and the non-local search result display region in the display order.

Optionally, the local search result display region is a collapsible display region. In this case, the information search apparatus may further include a local order determining module configured to: determine an order of the local search results in the local search result display region according to pre-estimated CTRs of the local search results, and determine a quantity of local search results displayed when the local search result display region is in a collapsed state.

Optionally, the non-local search result display region is a collapsible display region. In this case, the information search apparatus may further include a non-local order determining module configured to: determine an order of the non-local search results in the non-local search result display region according to pre-estimated CTRs of the non-local search results, and determine a quantity of non-local search results displayed when the non-local search result display region is in a collapsed state.

Optionally, the information search apparatus further includes: an operation log acquisition module, configured to acquire a click operation log of the user on the information search results; and an iterative optimization module, configured to perform iterative optimization on the non-local search preference determination model according to the click operation log.

According to a fourth aspect, an embodiment of the present disclosure further provides an information search apparatus applied to a client. The apparatus includes: an input monitoring module, configured to monitor a search word entered by a user; a search word sending module, configured to send the search word to a server; a search result receiving module, configured to receive information search results fed back by the server, where the information search results are obtained by the server through search by using a search policy corresponding to recognized search intention after the search intention of the user is recognized according to feature information of the search word and/or search context feature information, and the information search results include a local search result and/or a non-local search result; and a search result display module, configured to display the information search results.

Optionally, the information search results may include a local search result display region, a non-local search result display region, and a display order. The local search result display region may include one or more local search results, the non-local search result display region may include one or more non-local search results. Correspondingly, the search result display module may be specifically configured to display the local search result display region and the non-local search result display region on the same screen of the client in the display order.

According to a fifth aspect, an embodiment of the present disclosure further provides an information search system. The system includes a server and a client. The server includes the apparatus in the third aspect. The client includes the apparatus in the fourth aspect.

According to a sixth aspect, an embodiment of the present disclosure further provides a computer device, including a memory, a processor, and computer programs stored in the memory and executable by the processor. When executing the computer programs, the processor is caused to perform the steps of the method in the first aspect.

According to a seventh aspect, an embodiment of the present disclosure further provides a computer device, including a memory, a processor, and computer programs stored in the memory and executable by the processor. When executing the computer programs, the processor is caused to perform the steps of the method in the second aspect.

According to an eighth aspect, an embodiment of the present disclosure further provides a computer readable storage medium. The computer readable storage medium stores computer programs. When executing the computer programs, a processor is caused to perform the steps of the method in the first aspect.

According to a ninth aspect, an embodiment of the present disclosure further provides a computer readable storage medium. The computer readable storage medium stores computer programs. When executing the computer programs, a processor is caused to perform the steps of the method in the second aspect.

The embodiments of the present disclosure provide the information search method applied to a server. The method includes: receiving a search word sent by a user by using a client; recognizing search intention of the user according to feature information of the search word and/or search context feature information; performing search by using a search policy corresponding to the search intention and according to the search word to acquire information search results associated with the search word; and sending the information search results to the client, so that the client displays the information search results. In the present disclosure, search intention of a user is recognized, and search is performed according to a corresponding search policy. For example, both local search and non-local search are performed, or only non-local search is performed, so that search results that better satisfy the intention of the user can be obtained. In this way, when intending to perform non-local search, the user can obtain non-local search results without needing to switch a current positioning city, thereby effectively improving search efficiency and improving user experience.

To make the foregoing objectives, features, and advantages of the present disclosure more comprehensible, preferred embodiments are described in detail below with reference to the accompanying drawings.

