旅游服务的个性化推荐技术

旅游服务的个性化推荐技术(任务书,开题报告,论文18000字,答辩PPT)
摘要
近年来由于消费升级和移动互联网的普及，在线旅游业竞争越来越激烈，涌现了很多垂直型的旅游平台，各大OTA获客成本越来越高，除了互联网人口红利的逐步消失，还因为目前的消费需求越来越分散、个性化。面对繁杂的旅游产品信息，需要一个更“聪明”的机制来帮用户筛选适合自己的个性化产品。随着移动互联网的兴起，用户使用产品的时间和空间跨度都变大，产品的使用环境也增多，不同使用环境下用户的需求是不一样的。本文以去哪网为例，深入分析现有个性化推荐系统存在的关键问题，在这个基础上，本文提出了基于用户场景的个性推荐模型，通过分析新用户冷启动场景下用户的痛点以及需求的机会点，提出了相应的算法策略，并设计了衡量推荐系统的用户满意度方式。
关键词：在线旅游 个性化推荐 用户场景 用户冷启动

Abstract
In recent years, due to the consumption upgrade and the popularization of the mobile Internet, the online tourism industry has become more and more fierce, and many vertical travel platforms have emerged. The major OTA customers are getting more and more expensive, in addition to the gradual disappearance of the Internet’s demographic dividend, but also because The current consumer demand is increasingly dispersed and personalized. Faced with complex information on travel products, there is a need for a more "smart" mechanism to help users filter their personalized products. With the rise of the mobile Internet, the time span and the space span of users' use of products have increased, and the use environment of products has also increased. The needs of users under different usage environments are not the same. This paper takes the network as an example to analyze in depth the key problems existing in the existing personalized recommendation system. Based on this, this paper proposes a personalized recommendation model based on user scenarios, through the analysis of the user's pain points and needs in the cold start environment of new users. At the point of opportunity, a corresponding algorithm strategy was proposed and a user satisfaction method for measuring the recommendation system was designed.
KeyWords：Online travel；Personalized recommendation；User scene；User cold-start

目录
摘要    I
Abstract    II
1绪论    1
1.1研究的背景及意义    1
1.2国内外研究现状    2
1.2.1个性化推荐系统研究现状    2
1.2.2个性化推荐算法的研究现状    3
1.3研究内容和技术路线    4
1.3.1研究的基本内容    4
1.3.2拟采用的技术方案    4
1.3.3 研究目标    4
2推荐系统相关理论及技术概述    5
2.1在线个性化旅游    5
2.2个性化推荐系统    5
2.2.1个性化推荐系统架构    6
2.2.2个性化推荐系统信息源    6
2.2.3个性化推荐系统评测    7
2.3个性推荐算法    8
2.3.1基于物品的协同过滤算法    8
2.3.2基于内容特征个性化推荐    9
2.3.2隐语义模型算法    10
2.3.3基于社交网络的推荐算法    10
2.3.4热度算法    11
2.4基于用户场景的设计    11
3个性化旅游推荐系统关键问题    13
3.1目前主流旅游个性化推荐系统流程    13
3.1.1用户日志处理    13
3.1.2规则引擎    13
3.1.3需求排序    15
3.2旅游个性化推荐系统现存关键问题    15
3.2.1场景列举    15
3.2.2机会点挖掘    15
3.2.3设计策略    16
3.2.4情感化    16
3.2.5衡量标准    16
4基于用户场景的个性化推荐改进方案    17
4.1基于用户场景的推荐引擎核心架构    17
4.2获取用户数据及推荐策略    17
4.2.1引导用户选择旅游标签    17
4.2.2引用站外数据    19
4.3热门推荐    19
4.4实时反馈三层体系    20
4.4.1在线推荐    21
4.5排序算法    22
4.6衡量标准    22
5总结与展望    24
5.1总结    24
5.2展望    24
参考文献    26
致谢    28