Reservation Platform Search With Personalized Query Expansion
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Solution Overview
Problem
Users face difficulties in conducting effective searches on reservation or booking platforms due to inadequate or complex search tools, leading to complex search processes and irrelevant results.
Innovation Solution
Implementing search features such as autocomplete, autosuggest, and search expansion on platforms to provide localized, personalized, and relevant search results, using datasets and machine learning models to enhance user search experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search tools are used on reservation platforms, then the search process becomes complex and results become irrelevant, but adding more search features and personalization increases device complexity
Solution Approach 1:
The system automatically generates personalized search results by utilizing user profile data, search history, and machine learning models without requiring manual configuration of complex search parameters. The search expansion feature autonomously modifies search queries based on user behavior patterns, eliminating the need for users to manually adjust multiple search filters while still achieving highly relevant results.
Solution Approach 2:
The system dynamically adjusts search parameters by modifying the original query based on user profile attributes, search history, and real-time behavior. The search expansion module generates multiple modified queries with different parameters (location, date, price range, amenities) and selects the most relevant results, thereby improving search reliability without requiring users to manually change parameters.
2Productivity
If personalized search features are implemented, then search efficiency improves, but the system complexity increases
Solution Approach 1:
The system pre-processes user profile data, search history, and preference patterns before the actual search occurs. Machine learning models are trained in advance to recognize user intentions, and the system prepares personalized search parameters and result rankings ahead of time. This preliminary action enables fast, efficient personalized search results without requiring complex real-time processing during user interaction.
Solution Approach 2:
The system introduces machine learning models and automated query modification modules as intermediaries between the user's simple search input and the complex search database. These intermediary components automatically translate user intentions into optimized search queries, filter results based on personalized criteria, and present relevant outcomes, thereby achieving high search efficiency while shielding users from underlying system complexity.
3Quantity of substance
If multiple search result options are provided, then search completeness improves, but the search process becomes more complex for users
Solution Approach 1:
The system segments search results into different categories based on user preferences and search intent (e.g., top recommendations, alternative options, budget-friendly choices). Each segment is further divided by relevant criteria such as location, price range, or amenities. This segmentation presents multiple result options in an organized, digestible format that maintains search completeness while simplifying the user's navigation through results.
Solution Approach 2:
Instead of presenting all search results and requiring users to manually filter and sort through them, the system inverts the approach by pre-filtering and pre-sorting results based on personalized criteria. The system automatically determines which results are most relevant and presents them first, effectively doing the filtering work for the user and simplifying their interaction while maintaining comprehensive search options.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for improving user search experiences on an online searchable platform. Various aspects may include receiving a query input by a user of the platform. Aspects may also include generating, based on executing a search using the query, a search result including one or more listings. Aspects may also include determining at least a modified query based on attributes of the query, the search result, and a user profile. Aspects may also include generating, based on the modified query, a recommendation result including one or more recommended listings. Aspects may include displaying, at the client device, the recommendation result within the search result, wherein a placement of the recommendation result, for example, in a carousel format, is based on a relevance of the recommendation result to the user profile and the search.