Interactive Guide Assignment System with Destination Recommendation and Built-in Chatbox

Babina Banjara, Jinish Shrestha, Jinu Nyachhyon, Rijan Timilsina, S. Shakya
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Abstract

This proposed system provides a website called 'Safari Nepal', where users can search for destinations and check their location on a map. Users when registering on the website, can fill up the details about themselves and choose to either be a tour guide or a tourist. Based on the search and preferences of the user, similar destinations are recommended to the user via a recommendation system that uses a content-based recommendation feature. This feature works on the data obtained from the user, either explicitly or implicitly. The concept of K-Nearest Neighbours (KNN) and Cosine similarity makes the recommendation more accurate. KNN uses a distance algorithm that sorts from most liked destinations to least liked, based on the preferences of the user. This sorted list of destinations is further filtered by Cosine similarity, which is a measure of how similar two vectors in an inner product space are. It is calculated by taking the cosine of the angle between two vectors and determining whether two vectors are pointing towards the same general direction. Thus, combined KNN and Cosine similarity gives a better recommendation to the user. The map is integrated into the system using Mapbox API. Also, the system connects users with tour guides and gives them space to chat via a chatbox called ‘Travel Buddy’ where they can discuss further the destination, the amount charged by the guide, etc. The chatting feature on the system allows multiple users to connect and make conversations about the destination creating various chatrooms. In the system, the user can also publish their blogs describing their experiences and share their thoughts on particular destinations.
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带有目的地推荐和内置聊天框的交互式导游分配系统
这个拟议中的系统提供了一个名为“Safari尼泊尔”的网站,用户可以在上面搜索目的地,并在地图上查看他们的位置。用户在网站上注册时,可以填写自己的详细信息,选择做导游或游客。根据用户的搜索和偏好,通过使用基于内容的推荐功能的推荐系统向用户推荐相似的目的地。该特性对从用户获得的数据进行显式或隐式处理。k近邻(KNN)和余弦相似度的概念使推荐更加准确。KNN使用一种距离算法,根据用户的偏好,从最受欢迎的目的地到最不受欢迎的目的地进行排序。这个排序的目标列表通过余弦相似性进一步过滤,余弦相似性是衡量内积空间中两个向量的相似程度。它是通过取两个向量夹角的余弦值来计算的,并确定两个向量是否指向相同的大致方向。因此,结合KNN和余弦相似度可以给用户更好的推荐。地图通过Mapbox API集成到系统中。此外,该系统将用户与导游连接起来,并通过一个名为“旅行伙伴”的聊天框为他们提供聊天空间,在那里他们可以进一步讨论目的地,导游收取的费用等。系统上的聊天功能允许多个用户连接并就目的地进行对话,创建各种聊天室。在这个系统中,用户还可以发布他们的博客,描述他们的经历,并分享他们对特定目的地的想法。
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