AI-powered personalized travel recommendation system for tourists

Abstract

An AI-powered personalized travel recommendation system for tourists, consisting of: a user interface configured to receive user input regarding travel preferences, interests, budget constraints, and desired length of stay; a data processing unit comprising one or more machine learning algorithms configured to analyze user data, including but not limited to past travel behavior, demographic information, and real-time location data, to generate personalized travel recommendations; a recommendation engine that is operationally connected to the data processing unit and configured to synthesize and aggregate various data sources, including social media activity, online reviews, weather forecasts and local events, to compile a list of tailored destinations, activities, accommodations and dining options; an adaptive feedback loop that changes recommendations based on user interactions and feedback, with the feedback being used to improve the machine learning algorithms over time; a travel itinerary generator that uses the results of the recommendation engine to create an optimized itinerary, making it easier to discover popular tourist attractions, hidden gems, and off-the-beaten-path experiences; and a sharing module that allows users to share their personalized itineraries and ratings with other users of the system, thus promoting a community-oriented approach to travel planning.

Description

Field of the invention

[0001] The present disclosure relates generally to the field of travel technology, and more particularly to applications for artificial intelligence (AI), machine learning, etc., in personalized travel recommendations. The invention includes systems and methods for aggregating user data to automatically suggest areas of interest where a customer may want to travel, or collectively, accommodations with potential restaurants. More particularly, the invention relates to a mobile and web-based solution that improves vacation planning for travelers by offering personalized recommendations that change in real time according to personal preferences, interests, and contextual factors. These capabilities bridge multiple disciplines from data analytics and user experience design to social networking, shaping the future of personalized travel solutions. Background of the invention

[0002] In the age of globalization, traveling has become very easy and uncomplicated. Many people have fulfilled their dreams and traveled to other countries, but at the same time, they wish the trip could feel like home. The methods often used in traditional travel planning are the result of intensive research that doesn't always correspond to personal possibilities.

[0003] Travel system recommendations are often based on static algorithms or generic data and may not match each traveler's individual interests, budget constraints, or time constraints. Furthermore, travelers face information overload via online channels, which in turn leads to decision fatigue and causes them to overlook personalized answers in favor of unique experiences. We also need a smarter, more responsive system that combines cutting-edge technologies to provide personalized travel recommendations.

[0004] However, over the past twenty or more years, the travel industry has seen many changes brought about by technology and, later, widespread access to the internet. Far from it, the demand for travel recommendation systems is as high as ever. More and more customers want a unique and customized experience when planning their trip to any destination in the world. Traditional travel planning is often done using guidelines and books and can lack the personalization that truly reflects the individual's desires. Most current solutions rely on rule-based algorithms, where recommendations are generated based on predefined conditions. These result in universal recommendations that may not be suitable for everyone.

[0005] Most existing travel recommendation systems rely on user-generated content such as ratings and reviews (Yelp, Trip Advisor). While they collect a wealth of information, many find it impersonal or overwhelming due to the wide variety of presentation options. Furthermore, user-generated data can also create a bias: if there are more ratings or reviews for area A, they dominate the results and hide other locations in a different part of the map. Because of this bias, users miss out on hidden gems that might match their specific interests and desires. As vast as these platforms are, they don't do much more than data aggregation and arguably still lack the dynamic nature of changing preferences during planning or real-time information on local events.

[0006] This is also the most commonly used approach, which involves the use of recommendation engines with collaborative filtering. These systems learn user behavior based on interaction data from similar users and suggest travel options. While this approach can be effective, it is naturally limited by the need to have sufficient bottom-up user data. This is the crux of content discovery, as it could mean that new users or others with less general preferences are left out of recommendations.Second, collaborative filtering cannot account for the myriad contextual influences that shape people's travel decisions, such as weather conditions at a given time (or those expected for some future time), seasonal events planned by cities or resorts, and yes, even somewhat subjective qualities like how happy / relaxed / stressed / excited / annoyed / triumphant someone is feeling when searching for their next perfect getaway.

[0007] Machine learning is seen as a beacon of hope for addressing the long-standing shortcomings of traditional recommendation systems. Yet most conventional machine learning models fail to address this challenge of personalization. For example, cluster-based systems can group users into broad categories and display suggestions that are more personalized than rule-based ones, yet still very general. While some more advanced systems leverage user feedback for improvements, their processes may react slowly to changes in consumer tastes. This delay can result in recommendations that are outdated or do not reflect users' current interests.

[0008] In fact, social media has added another dimension to travel planning, with informal platforms like Instagram and Facebook igniting the spark of inspiration. Platforms allow users to share their travel experiences with others and introduce new destinations in a visual (and sometimes stunning) way. While social media can influence travel decisions, it generally concentrates points of interest into a scattered view, meaning it presents quite an organizational hurdle when planning a trip. This can often lead to travelers spending hours trawling through posts and hashtags trying to find useful information that aligns with their preferences. Furthermore, the amount of content can also lead to decision fatigue, where users feel overwhelmed by the choices.

[0009] Others incorporate real-time data such as weather and local events into their recommendations. This method has some situational applicability, but its utility is generally aesthetic. However, many systems continue to neglect the ability to adapt and evolve recommendations based on real-time user interactions or changing preferences. For example, a user may express interest in outdoor activities but choose cultural activities due to bad weather. However, not all new preferences are incorporated, as these technologies may be incompatible with our existing infrastructure, resulting in a less than seamless user experience.

[0010] Furthermore, the emphasis on technical capabilities often leads to usability being neglected. The problem with many travel recommendation systems is that they have unnecessarily complicated user interfaces. A clunky user interface slows down input speed, which in turn reduces the effectiveness of recommendations. This is because usability is often a top priority for travelers, making planning easier.

[0011] Furthermore, providers on the market often lack many of the social aspects that contribute to building a community among fellow travelers. While certain platforms allow users to share itineraries and reviews, the elements of interactivity and social connection are often lost. These disconnects lead users to miss opportunities to collaborate with others who share travel interests or even learn from the experiences shared in the community. Their strong social offering can enhance the entire trip planning process by not only inspiring travelers but also allowing them to benefit from practical advice shared with other like-minded individuals.

[0012] The current state of the travel technology landscape leaves much to be desired, as is often the case with emerging solutions. They generally have limited ability to offer true, trust-based personalization and contextual recommendations for tourism. User-generated content and collaborative filtering can generate biased tendencies and generic recommendations, while traditional machine learning norms struggle with real-time adaptability, hindering user engagement. Social media provides us with ideas without a clearly organized planning approach, and often the user experience is ruined by distraction from complex interfaces.These limitations illustrate the current state-of-the-art technological capabilities, which do not offer a holistic approach to personalized travel planning. They therefore highlight the need to move away from previous paradigms toward an advanced AI-driven solution with sophisticated, user-centered design. This is a system that harnesses the full power of data while also responding instantly to your individual preferences, resulting in more valuable and enjoyable travel experiences for tourists of the invention. Summary of the invention

[0013] Embodiments of the present invention provide such a system that leverages AI techniques to optimize travel planning by offering personalized recommendations based on preferences, interests, and past travel history. Using sophisticated machine learning algorithms, it mines a treasure trove of user data, past travel experience, demographic information, and current location to provide tailored recommendations for destinations, activities, accommodations, and dining options. The system integrates various data sources, including social media interactions and reviews from online applications, weather forecasts, or events at a particular location. It provides dynamic, real-time suggestions based on users' evolving preferences.Seventy VN, a member of the Y Combinator (YC) Winter 2016 class, allows users to input factors such as budget, what they want to do on their trip—for example, adventure, relaxation, or cultural experiences—and how long they can stay. It generates a curated itinerary with must-see attractions and secret spots. Furthermore, the AI ​​trains with user feedback, reducing false positives and determining intent over time. There's a social dimension where users can share their personal travel plans and user feedback, building an engaging travel audience. This new system not only simplifies trip planning but also enhances your travel experience, offering tailored services to make your trip even more enjoyable.

[0014] The main objective of the invention is to provide personalized travel suggestions and recommendations based on tourists’ interests and travel history, according to their individual preferences, using an AI-powered system.The system is designed to improve travel planning through machine learning by incorporating multiple and diverse data sources, user-provided inputs, historical information about a user's past trips, demographic details (inferred or explicit), social media activity of both the users in question and their friends, online reviews for venues / locations linked to candidate suggestions made at various points during the conversation threads, and also automatically extracted from web pages accessed on behalf of the account holders by another MSR-based service called Mixview, which supports an iterative process where new data is added with each fetch, including contextual factors such as weather or current locations immediately prior to the fetch.Another goal is to develop a system that changes according to user preferences, so that visitors can be offered suggestions not only throughout their conversation but also as they change tasks.

[0015] Furthermore, the invention aims to improve user experience by allowing travelers to easily enter information (budget and desired experience) via a convenient user interface during the trip planning process. This is intended to make travel discovery less tedious and overwhelming by offering an optimized itinerary that showcases a mix of worthwhile experiences and some off-the-beaten-path gems. Best of all, the invention incorporates a feedback loop that grows and evolves over time, with more user interactions providing a continuous improvement edge to refine the recommendations.

[0016] Another key goal is to create a community among travelers with social features that allow users to share curated itineraries and experiences and rate each other. This social component not only promotes user engagement but also fosters the development of a community where users can collaborate and inspire each other. In practice, the invention aims to improve tourists' current travel planning by providing a universally customizable and personalized travel recommendation system that supports tourists in their pre-trip research for a better and enhanced tourism experience. SHORT DESCRIPTION OF THE FIGURE

[0017] These and other features, aspects, and advantages of the present invention will become more readily understood when the following detailed description is read in conjunction with the accompanying drawings, in which like characters represent like parts throughout. Fig. Figure 1 shows a block diagram of an AI-based personalized travel recommendation system for tourists.

[0018] In Fig.Figure 1 shows a block diagram of an AI-powered personalized travel recommendation system for tourists. The system 100 includes: a user interface 102 configured to receive user input regarding travel preferences, interests, budget constraints, and desired length of stay; a data processing unit 104 with one or more machine learning algorithms 104a configured to analyze user data, including but not limited to past travel behavior, demographic information, and real-time location data, to generate personalized travel recommendations;a recommendation engine 106 operatively connected to the data processing unit and configured to synthesize and aggregate various data sources, including social media activity, online reviews, weather forecasts, and local events, to compile a list of tailored travel destinations, activities, accommodations, and dining options; an adaptive feedback loop 108 that modifies the recommendations based on user interactions and feedback, using the feedback to improve the machine learning algorithms over time; an itinerary generator 110 that uses the output of the recommendation engine to create an optimized itinerary, facilitating the discovery of popular tourist attractions, hidden gems, and off-the-beaten-path experiences;and a sharing module 112 that allows users to share their personalized itineraries and reviews with other users of the system, thus promoting a community-driven approach to trip planning.;

[0019] In one embodiment, the user interface 102 also includes a visual map component that displays recommended destinations and routes based on the generated itinerary, enabling interactive exploration of the suggested locations.

[0020] In one embodiment, the machine learning algorithms 104a are configured to employ collaborative filtering techniques, enabling the generation of recommendations based on the preferences and behaviors of similar users within the system.

[0021] In one embodiment, the recommendation engine 106 integrates a natural language processing module to analyze user-generated content from social media platforms and online reviews to improve the relevance of recommendations based on real-time trends and sentiment.

[0022] In one embodiment, the adaptive feedback loop 108 includes a reinforcement learning mechanism that dynamically updates the system's recommendation model based on quantified user satisfaction metrics derived from user feedback and behavioral analytics.

[0023] In one embodiment, it also includes an API (Application Programming Interface) module that enables seamless integration of third-party services, such as booking platforms and transportation services, into the user interface for real-time travel planning.

[0024] In one embodiment, the itinerary generator 110 is configured to optimize the proposed itinerary based on predefined constraints, including, but not limited to, time availability, travel distance, and budget limits, thereby ensuring that the generated itinerary is practical and feasible.

[0025] In one embodiment, the sharing module 112 enables social media sharing functionality so that users can post their personalized itineraries and reviews directly to their social media accounts, thereby improving community engagement and visibility of the system.

[0026] In one embodiment, the recommendation engine 106 uses geofencing techniques to provide the user with location-based recommendations and notifications and to alert the user to nearby attractions or events in real time based on their current location.

[0027] In one embodiment, it also includes a user profile management module that securely stores user preferences, travel history, and feedback, enabling personalized experiences across multiple sessions.

[0028] The AI-powered system of personalized travel recommendations for tourists was developed to transform the way people plan their trips, using sophisticated machine learning algorithms and data integration techniques. At its core is an interface through which tourists can communicate their travel interests, as well as any budget constraints and travel duration, etc., they have in mind for a destination. This data is very important, as all personalized recommendations are based on this information.

[0029] The system contains a powerful processor with machine learning algorithms capable of interpreting a rich list of user data points, from historical travel patterns and demographic data to real-time location information. These utilized collaborative filtering algorithms and were designed to identify patterns of similarity between users and recommend personalized itineraries tailored to their individual preferences. A system that combines explicit data (the user's stated preferences) with implicit information from how they use the system determines a set of recommended destinations, activities, or lodging and dining options, reflecting which types of experiences seem most likely to match those desires.

[0030] In addition to tailored suggestions, the recommendation engine also reads and aggregates a wide range of data on social media activity, online reviews, weather forecasts, and local events. The recommendation engine also includes a natural language processing module for analyzing social media reviews and comments. Red Sand uses machine learning techniques on a range of global user location and social information for real-time analysis to stay up-to-date on current trending topics and traveler sentiment. This ensures that discovery recommendations align with users' most recent experiences.

[0031] It features a learning engine with an adaptive feedback mechanism that constantly refines itself by continuously ingesting what it learns from user behavior and reactions. This system uses a reinforcement learning framework to gradually update its recommendation model with quantized versions of user satisfaction with item selections. This allows the algorithms to adjust their output based on user feedback, resulting in more and better relevant suggestions over time. The inclusion of a travel route generator also improves the user experience, as it automatically processes the generated recommendations and provides an optimized, ordered version of them.This itinerary allows you to explore not only popular tourist attractions, but also those must-see spots, the bucket-list but unmissable ones we all love to photograph to cement our vacation status with friends and family back home. Backstreet tours also mean you get a solid insider's look at experiences off the beaten track, so combine the two for a richer touring experience.

[0032] It features a sharing module that encourages fellows' participation in the community by creating personalized itineraries and sharing reviews with other travelers. People can interact with each other, creating a sense of community where they can share feedback or recommendations from personal experiences. These sharing features also include social media platforms to spread these curated lists and raise awareness about the system in general, with new users hopefully adding strength.

[0033] The algorithm then takes the suggested travel routes and optimizes them to make them more practical or feasible by incorporating predefined constraints such as time availability, travel distance / OSRM isochrony, and budget limits. This ensures that users receive recommendations based on places they are likely to actually enjoy visiting and that are accessible within their travel constraints. Furthermore, it uses geofencing technology, which enables location-based suggestions and real-time notifications. This means users are constantly alerted to nearby attractions or events in their travel environment, adding a level of spontaneity and excitement to otherwise mundane experiences.

[0034] The system includes a user profile management module that stores preferences and travel history from feedback to more securely personalize experiences across subsequent sessions and devices. The advantage of this is that users can access the system and see some recommendations based on their previous interactions—meaning seamless, personalized trip planning for each user. In summary, the AI-powered personalized travel recommendation system for tourists uses the latest algorithms combined with data integration techniques to optimize the travel experience, making trip planning more effortless and enjoyable at both ends of the spectrum while being tailored to each user.

[0035] The personalized, AI-powered travel recommendation system consists of a series of interconnected components, all designed to work harmoniously together. It is essentially a highly advanced machine learning engine that manages and interprets user information. The engine enables such ranked listings based on factors such as collaborative filtering and natural language processing to understand user preferences and previous behavior with inputs from real-time data streams. For example, when a user enters their travel preferences through the interface, this system can instantly search large databases containing years of historical data to identify patterns and subsequently correlate this information with current events or local points of interest.

[0036] To this end—and herein lies the problem—the system connects to social media platforms to leverage user data to gain a more accurate understanding of their options and thus learn more about them. Online reviews from travel websites and social media also contribute to a deeper understanding, providing the system with additional information about how popular certain destinations or services are. The app uses weather forecasts to tell you when the best time of year to visit a location is, and links to local event calendars help travelers learn about things happening in a particular location that might enhance their visit.

[0037] Through an intuitive and user-friendly mobile and web interface, upon entering the system, you'll be prompted to complete a questionnaire about your travel preferences, such as preferred activities, average budget, and vacation length. These inputs are then processed (compared with the extensive data store) to create a customized itinerary. User interaction with the system (e.g., widgets to identify interesting activities, saved favorites, etc.) is used in real time by our recommendation engine to appropriately release new iterations for this itinerary.

[0038] The system wouldn't work without feedback loops. We let users rate and provide additional insights or recommendations to other readers so we can all make the most informed decisions possible. This feedback is then used to improve the algorithms and inform how we adapt as processes are refined—essentially, modern digital creatures. The community dimension means users can save their own itineraries, but also share them. This builds a social layer over the service, helping travelers connect with others to save and learn.

[0039] In short, the AI-powered personalized travel recommendation system is a flexible, user-centric tool for travel planning. Personalized, real-time recommendations are based on sophisticated technology and a variety of data sources. This makes travel planning easier and more enjoyable than ever before, as it's perfectly tailored to individual tastes.

[0040] The present invention relates to travel technology, and more particularly (without limiting the scope) to personalized travel recommendation systems based on artificial intelligence or machine learning. This complex technology, incorporating advanced algorithms and data processing methods, studies users' habits, likes, and dislikes and extracts information from other external sources to provide personalized travel destination recommendations. By creating a solution using data analytics that generate live recommendations, the invention provides tourists with personalized answers to their travel planning questions. This system has particular significance in the rapidly growing field of smart tourism, as it helps provide personalized services, which are increasingly becoming a necessity rather than an option for an enriching travel experience.This combination of UI design, data integration, and machine learning makes this invention one of the most important stepping stones on the path to where travel planning meets technology and devices. REFERENCE 100 systems QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited non-patent literature

[0000] Seventy VN is a member of the Y Combinator (YC) class of Winter 2016

[0013]

Claims

[1] An AI-powered personalized travel recommendation system for tourists, consisting of: a user interface configured to receive user input regarding travel preferences, interests, budget constraints, and desired length of stay; a data processing unit comprising one or more machine learning algorithms configured to analyze user data, including but not limited to past travel behavior, demographic information, and real-time location data, to generate personalized travel recommendations; a recommendation engine operatively connected to the data processing unit and configured to synthesize and aggregate various data sources, including social media activity, online reviews, weather forecasts, and local events, to compile a list of tailored travel destinations, activities, accommodations, and dining options; an adaptive feedback loop that changes recommendations based on user interactions and feedback, using the feedback to improve machine learning algorithms over time; an itinerary generator that uses the results of the recommendation engine to create an optimized itinerary that makes it easier to discover popular tourist attractions, hidden gems, and off-the-beaten-track experiences; and a sharing module that allows users to share their personalized itineraries and reviews with other users of the system, thus promoting a community-oriented approach to travel planning. [2] The system of claim 1, wherein the user interface further comprises a visual map component that displays recommended destinations and routes based on the created itinerary, thus enabling interactive exploration of the suggested locations. [3] The system of claim 1, wherein the recommendation engine integrates a natural language processing module to analyze user-generated content from social media platforms and online reviews, thereby improving the relevance of the recommendations based on real-time trends and sentiment. [4] The system of claim 1, wherein the adaptive feedback loop comprises a reinforcement learning mechanism that dynamically updates the system's recommendation model based on quantified user satisfaction metrics derived from user feedback and behavioral analytics. [5] The system of claim 1, wherein the itinerary generator is configured to optimize the proposed itinerary based on predefined constraints, including, but not limited to, time availability, travel distance, and budget limits, thereby ensuring that the generated itinerary is practical and feasible. [6] The system of claim 1, wherein the sharing module provides social media sharing capability, allowing users to publish their personalized itineraries and reviews directly to their social media accounts, thereby improving community engagement and visibility of the system. [7] The system of claim 1, wherein the recommendation engine uses geofencing techniques to provide the user with location-based recommendations and notifications and to alert the user to nearby attractions or events based on their current location in real time. [8] The system according to claim 1 further comprises a user profile management module that securely stores user preferences, travel history and feedback, enabling personalized experiences across multiple sessions and devices.

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