临平区旅游场所个性化推荐数据

临平区旅游场所的个性化推荐数据可以广泛应用于提升旅游体验、优化旅游规划、增强旅游营销效果、丰富教育内容、辅助政府决策支持等多个方面。通过算法加工，这些数据能够为游客提供量身定制的旅游体验推荐，如根据游客的偏好推荐临平区的特色旅游路线和活动，增强旅游体验的同时，促进当地文化的交流和传播。此外，数据还能帮助旅游企业优化营销策略，提高旅游产品的吸引力和市场竞争力。同时，这些数据也能为政府提供决策支持，如通过分析旅游数据来规划和改进旅游设施，提升公共服务质量。1.数据收集和清洗：根据给定的用户ID访问“临平区文化活动参与者个体画像数据”数据库，获取用户的兴趣领域和活动偏好。从公司文化保障卡服务系统中抽取临平区所有旅游场所的类型特征，以及用户打卡次数的历史数据。通过数据清洗去除无效或错误记录，确保数据质量。
2.用户兴趣匹配和推荐候选生成：利用余弦相似性算法，将用户的兴趣领域和活动偏好与旅游场所的类型特征进行匹配。计算余弦相似性的值，该值代表兴趣匹配度。根据兴趣匹配结果，为用户生成一个推荐的候选场馆列表。
3.推荐候选排序：对候选场馆列表中的每个场馆，根据用户的兴趣匹配度、场馆历史打卡次数计算一个偏好评分，计算过程为：偏好评分=w1×余弦相似性（代表兴趣匹配度）+w2×场馆历史打卡次数（事先对所有场馆的历史打卡次数进行归一化处理）；w1、w2是权重系数，用于调整兴趣匹配度和场馆历史打卡次数在偏好评分中的重要性。根据偏好评分，对候选场馆进行排序。
4.推荐列表生成：选择排名靠前的3个场馆，生成最终的推荐列表。

The personalized recommendation data for tourist attractions in Linping District has wide applications in multiple scenarios, including enhancing tourist experience, optimizing travel planning, improving tourism marketing effectiveness, enriching educational content, and supporting government decision-making. Processed via algorithms, this data can provide tourists with tailored tourism experience recommendations, such as recommending characteristic tourist routes and activities in Linping District based on tourists' preferences, which not only enhances the tourist experience but also promotes the exchange and dissemination of local culture. Additionally, the data can help tourism enterprises optimize their marketing strategies and enhance the attractiveness and market competitiveness of tourism products. Meanwhile, this data can also provide decision-making support for the government, such as planning and improving tourism facilities and enhancing the quality of public services by analyzing tourism data.
1. Data Collection and Cleaning: Access the database of "Individual Profile Data of Cultural Activity Participants in Linping District" using the given user ID to obtain the user's interest areas and activity preferences. Extract the type characteristics of all tourist attractions in Linping District and the historical data of users' check-in times from the corporate cultural security card service system. Remove invalid or erroneous records through data cleaning to ensure data quality.
2. User Interest Matching and Recommendation Candidate Generation: Use the cosine similarity algorithm to match the user's interest areas and activity preferences with the type characteristics of tourist attractions. Calculate the cosine similarity value, which represents the interest matching degree. Generate a recommended candidate venue list for the user based on the interest matching results.
3. Recommendation Candidate Ranking: For each venue in the candidate venue list, calculate a preference score based on the user's interest matching degree and the venue's historical check-in times. The calculation formula is: Preference Score = w1 × Cosine Similarity (representing interest matching degree) + w2 × Normalized Historical Check-in Times of the Venue (the historical check-in times of all venues have been normalized in advance); w1 and w2 are weight coefficients used to adjust the importance of interest matching degree and historical check-in times of the venue in the preference score. Rank the candidate venues based on the preference score.
4. Recommendation List Generation: Select the top 3 venues with the highest rankings to generate the final recommendation list.

搜集汇总

数据集介绍

特点

该数据集包含600条临平区旅游场所的个性化推荐数据，通过余弦相似性算法和偏好评分对候选场馆进行排序，最终生成推荐列表。数据适用于提升旅游体验、优化旅游规划和营销策略等多个应用场景。