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Keyword Query Suggestion Techniques With Artificial Intelligence Perspective

       R. Ravinder Reddy1*, C. Vaishnavi1, Ch. Mamatha2 and S. Ananthakumaran3

       ¹ Chaitanya Bharathi Institute of Technology, Hyderabad, India

       ² Software Engineer, Hyderabad, India

       ³ Koneru Lakshmaiah Education Foundation, Vijayawada, India

       Abstract

The user is interested in retrieving the more relevant and useful information from the search engines; to get this, we need an appropriate query to search. Framing an appropriate query, which is based on some suggestions, is more important in the fast-growing ICT world. In these days, the user-specific and location-based queries are more relevant. With the huge adoption of mobile and handheld devices in our regular life, the pace of search engines has changed, and every user is expecting more appropriate search results for him; based on this, many recommendation systems are working. Artificial Intelligence (AI) has changed in many aspects of the human being. In this work, we are using the AI for query suggestion based on the user relevant information, and it gives more accurate results. It has changed the query suggestion strategy. Most of the mobile and handheld devices contain user data and their preferences. The existing search engines are working based on the page rank principle. But, the perspective has changed due to the mobile devices and Global Positioning System (GPS) services, with the increased usage of location-based devices and the availability of the internet, which prompted us to work on this problem. Most of the existing search engines help the user to get the required data based on the user query, but not based on the location. The query suggestion will help the users with precise query suggestions to search on the web. While searching on the web with an appropriate query will retrieve the good results. The query suggestion is a key reason in the search engines to optimize performance. As the usage of mobile devices increased in the recent past, the query search has been reformed to the location-based query suggestion. Especially, searching the query based on a particular location will avoid the burden on the search engine and produces the more appropriate results to the user. Location-based query suggestion is crucial in these days, many of the businesses like travel, hotel, hospital, tourism, and banks required user location. The location access and awareness resolve many query suggestions based on the querying efficiency and exactness of the result. The addition of AI perspective to this location-based system makes it adaptable to human life and provides them useful information based on user location, time, and previous search information.

      Keywords: Artificial intelligence, query suggestion, location-aware keyword, search engine

2.1 Introduction

      The enormous growth of ICT in the past two decades has changed the human lifestyle a lot. With the advent of fast-changing technologies that makes us more comfortable and to take fast decisions, the time constraint is becoming more critical [9]. The increased availability of the internet and pervasive computing has changed the computing paradigm. Most of the queries can be solved in minutes based on user preferences. These days, everyone is using the internet from any corner of the world without having any particular domain knowledge. It becomes a challenge for the researchers to provide appropriate and more useful query results to the users. Most of the search engines are working to offer useful information to their clients. The retrieved information is very crucial and the precision of the results is more important. In the early age of search engines, they retrieved the data based on the page ranks [5]. But, these days, the location of the user is also more important along with the query.

The primary goal of the search engines is to find the more appropriate or proximity document from the search engine. The basic idea of a search engine is a rudimentary search that will find the documents and rank the documents which are found in the search criteria. The suggestion engine uses a popularity score to determine which queries to suggest. With the exponential growth of the internet, searching for resources on the web (like, data, files, articles, etc.) is very common these days. Search engine efficiency is becoming a key factor in the web search. The effective way of improving the efficiency of the search engine is by using the keyword query suggestion [16]. Keyword suggestion is the most fundamental feature of the search engine. Users normally submit short queries to the web search engine, and short queries are mostly ambiguous. The major problem of the current web search engine is that search queries, as they are short. Users try different queries to retrieve the relevant information because the user may have little knowledge about the information of searching. The provided list of keywords by the web search engine may not be a good description of the information needs of the user [18-20].

      The primary goals of the search engines are

      1 Effectiveness (quality)

      2 Efficiency (speed)

The growth and the significance of any search engines depend on these parameters only. Once the quality and speed of the search engines improve, the system performance will improve. Current research is toward the development of these performance measures. The success of any search engine is in a large part determined by the fact whether a user can find a good answer for his search query or not. That is why the most important aim of every search engine is to continuously improve its search performance. A lot of different techniques, architectures, algorithms, and models were invented and implemented to provide accurate search results that users consider as relevant and interesting. The basic model of the search engine has shown in Figure 2.1.

      To swamp this problem, many search engines have implemented the query suggestion method. Also, known as keyword suggestion. The effective method for keyword suggestions is based on data from the query log [1]. This log is maintained by the search engines from the previous queries. These logs maintain lots of data with page ranks and the server address [15, 24, 25]. Location is not maintaining in many of the log databases. Need to implement the location of the user in some specific queries. In the scenario of a user that is searching for food in the afternoon, we need to suggest a hotel that is nearby his location, if the same query is asked in the morning session, we need to suggest a good hotel that serves breakfast. The spatial location of the user is critical in this case. The query suggestion is along with the location is important, to support more accurate results [17]. The main goal of the spatial keyword is to suggest more effortlessly to find appropriate results that will placate all the situations concerning the circumstances of a search. Searching motivated to develop methods to recover spatial objects.

Figure 2.1 General architecture of a search engine.

      The main aim of the Artificial intelligence (AI) in the query suggestion is to automate the query suggestions based on the user circumstances. AI agents will learn the things based on the previous user preferences and locations; based on this, it will automate a query to the search engine and it recommends more accurate results to the user. It will help the user like a guide in specific applications based on his preferences. The AI agent learns the things from the user’s data and frames the appropriate query to get accurate results.

The advent of mobile devices and access to the internet in these devices makes us search location-based queries. Enhanced growth in the usage of the mobility devices has increased as shown in Figure 2.2. In the early age of computing, searches engines worked only on the keyword query-based searches. But, with the respect of these locations changes us to prompt for the location-aware keyword query is more significant these days. The mobile apps for transportation like Uber,

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