Computational Analysis and Deep Learning for Medical Care - Группа авторов

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would turn to query suggestion; it depends on circumstances. In order to investigate when and how the user uses query suggestion, we analyzed three kinds of datasets obtained from a major commercial web search engine, comprising approximately 126 million unique queries, 876 million query suggestions, and 306 million action patterns of users. The analysis shows that query suggestion is often used.

      1 When the original query is a rare query [8].

      2 When the original query is a single-term query,

      3 When query suggestions are unambiguous,

      4 When query suggestions are generalizations or error corrections of the original query, and

      5 After the user has clicked on several URLs in the first search result page.

      The search engines are working to provide better query suggestion input, and that they should dynamically provide query suggestions according to the user’s current state. There are different types of approaches for keyword query suggestion. This can be classified into three categories: random walk–based, cluster-based, and learning to rank approaches. We briefly review the other methods from our observation; any of the given methods cannot consider the user location in query suggestion.

       2.3.1.1 Random Walk–Based Approaches

      This method uses graph structure for modeling the information that is provided by query log and then applies the random walk process on graph for query suggestion [6, 7, 10, 11].

       2.3.1.2 Cluster-Based Approaches

       2.3.1.3 Learning to Rank Approaches

      This approach is trained based on different type of query features like query performance prediction [14]. Given query q, a list of suggestion is produced based on their similarity to q in topic distribution space. Query recommendation is a core task for large industrial search engines. The query recommendations is mostly depends on the query similarity measures. These measures can be used for query expansion or query clustering.

      2.3.2 User Preference From Log

      1 Modification of the user query

      2 Expansion

      3 Deletion

      Modification: user modifies the last term of the query [12]:

      {wi1, …wim} → {wi1, …w′im}, e.g., “single ladies song”→“single ladies lyrics”.

      The formation is done based the document frequencies and their proximities.

      Expansion: user adds one term to the end of the query:

      {wi1, …wim} → {wi1, …wim, wi(m+1)}, e.g., “sports illustrated” → “sports illustrated 2010”.

Type User activity Pattern
Modification 1. q:{single ladies song}2. q:{single ladies lyrics} 3. URL click Song → lyrics
Expansion 1. q:{sports illustrated}2. q:{sports illustrated 2010}3. URL click ɛ → 2010
Deletion 1. q:{ebay auction}2. q:{ebay} 3. URL click auction → ɛ

      Deletion: user removes the last term of the query:

      {wi1, …wi(m−1), wim} → {wi1, …wi(m−1)}, e.g., “ebay auction” → “ebay”.

      These keyword query refinements help the user to get the appropriate results of the user search.

      2.3.3 Location-Aware Keyword Query Suggestion

      Query suggestion is not based on the keyword, as the preferences have been changed with the location refinements. Here, the location of the user makes the change in the formation of the query.

      {feeling hungry, hotel near me} → {Hotel + Location}

      The formation of the query has changed based on the location of the user only; it would not search the entire city or the country. It takes the search for nearby proximity only. It makes the query more effective.

      2.3.4 Enhancement With AI Perspective

      In this approach, we consider every aspect of the user that helps us in making the content specific to the user. In the current era of smartphones, we have a greater chance of knowing the user more. With the request to access the user’s location, contacts, images, messages, etc., we can have a complete picture of where the user has been to, what he likes, what is his/her daily schedule, what he might be interested in, and what he can afford for. All the above information solves half the problem of personalization. Within the search engine or website, we also track users’ search history and his choice of websites depending on the click-throughs. For each query, AI enhances ranking factors that change from query to query, as the algorithm learns from how people choose search results and decides on the best-factors to take into account for every search. The next action of AI-powered search will always be better than the present one as it learns from the user and gets auto-tuned to his choice. By adding the AI content in the module the query becomes like this.

      {Feeling hungry, hotel near me} → {Hotel + Location + user preferences (vegan/Continental)}

       2.3.4.1 Case Study

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