with the access to user’s location, messages, profile, etc. Within his budget, the AI component can recommend hotel to him with appropriate location and price. It will save him lot of time, in many of the cases user may miss some of these, but the AI component will consider all aspects and recommends the suitable based on building the appropriate query to the user. The system should learn from the user’s feedback and become adaptive to the user in a better way.
Figure 2.3 AI-powered location-based system.
The enhancement that can be bought with AI:
1 Personalization
2 Sending Adaptive Notifications
3 Analyzing
4 Reduce labor at routine jobs
2.3.4.1.1 Personalization
This is our top priority and sole reason for our success. Analyzing the user should not be restricted to the platform in which our AI is being deployed. We should understand users and engage with them, anywhere and at any time, i.e., like accessing messages or the usage of Google maps or any other application with permission from user.
2.3.4.1.2 Sending Adaptive Notifications
In the modern era, we can gain the attention of user’s information need by sending the appropriate notifications based on their current location, time, previous searches or searches of users with similar requirements, etc.
2.3.4.1.3 Analyzing
AI processes and interprets patterns in data very efficiently. It replaces any search strategist and makes decisions with a higher accuracy. AI can take inputs from market trends, performance noticed, customer reports, etc. It does not leave any factor unnoticed.
2.3.4.1.4 Reduce Labor at Routine Jobs
We can always obtain a higher output by replacing the labor intensity at routine jobs and using the same at places where skill is required. Time and labor saved here can be used where creativity is to be employed. When replaced, we should always check speed and engagement rates with proper audit.
2.4 Architecture
Most of the businesses are running based on the mobile apps in these days. It gives the importance of the location for a search query. Generally, the architecture of the location-based keyword query suggestion has shown in Figure 2.4. It works on the principle of the location as well as the keyword query. It consists of three different modules; these can be used to recommend a query to the user. Along with the query, it takes the user location; based on this location, it retrieves the query and returns the results based on user locations like restaurant, hospital, and tourist spots.
Figure 2.4 Architecture diagram for querying.
2.4.1 Distance Measures
Measuring the distances in the location-aware keyword query suggestion is an important problem. The distance function will be crucial parameter in deciding the précised results. The decision of the results which we obtain will create the issue for the obtained outcome. In the following, Table 2.3 listed the different measures which are used in measuring the documents. Measuring the distance for spatial data and the keyword data are important in this case. The relevance of the retrieved results is very crucial in measuring the performance of the techniques. So, the relevance of the measures is so important in the document. We analyzed the different papers which are used for the different proximity measures for analyzing the relevance.
As shown in Table 2.3, many techniques will provide the user to retrieve the query; based on the different approaches, these may be good for some queries, and it may depend on the retrieved document based on the retrieval of the query [23]. The query suggestion is so important in these days; based on the location, their location, their habits, and interests may change like food preferences, usage items in their locations, it will be purely depends on the perception of the user’s location.
Table 2.3 Different approaches for the query suggestion techniques.
| S. no | Techniques |
| 1 | Index |
| 2 | Rank |
| 3 | Popularity |
| 4 | No of times referred |
| 5 | Index + location |
| 6 | Document proximity [4] |
| 7 | AI-based search |
| 8 | Keyword-document graph |
The different measures, which are used in the query suggestion techniques, are listed here.
1 Euclidean
2 Manhattan
3 Cosine similarity
4 Jaccard coefficients
The AI perspective will consider the following measures in query preparation, to enhance the performance of the query suggestion.
1 User’s information
2 Location
3 Previous search history
4 Back links
5 Keywords
6 Click through rate
7 Choice of websites
8 Other similar users’ choice
The AI algorithm learns from the results and decides the importance to be given to each of the factors specific to the user location. An AI-powered search engine learns and adjusts itself based on the ambiguous search queries; and it uses feedback data to improve the accuracy of its results.
2.5 Conclusion
Search engine algorithms begin incorporating esoteric information in their ranking algorithms. The tendency of the keyword query suggestion has been replaced by the user log to the location of the user. The user expects more accurate query results. The user needs to provide a single keyword query and location, the system it returns the results considering the user proximity location. Upgrading the system to further levels by adding the AI perspective, the query suggestion has changed to user preference level. The value of AI-powered search