旅游产品网络广告的个性化推荐研究

发布时间：2018-06-12 20:27

本文选题：旅游网络广告 + 个性化推送　；参考：《湖南工业大学》2017年硕士论文

【摘要】：旅游网络广告是加快旅游业信息化进程的关键手段之一,而旅游个性化信息服务是提升网络广告效果的重要方法。然而,愈来愈多的旅游产品、旅游方案的积累和在线旅游人数的暴增,不仅造成在线游客需要花费较长时间寻找满足自己需求的内容,还导致旅游网站信息量过载,造成在线游客时间成本增加和网站广告推荐效率下降。鉴于此,本文依据艾宾浩斯遗忘规律和马太效应现象,提出相应的广告个性化推荐优化技术,旨在提高推荐的精准度。首先在游客兴趣变化遵循艾宾浩斯遗忘规律的基础上,将兴趣遗忘函数融入协同过滤算法中,赋予游客评分时间权重,以此削弱历史评分的权值加强当前评分的重要性。然后分析了推荐系统中马太效应对游客兴趣预测的影响,将旅游产品的流行度引入协同过滤算法,加大对热门产品的惩罚值,降低流行度对游客兴趣相似度的影响。最后,从游客历史评分信息和产品隐含信息对游客兴趣预测的影响出发,结合艾宾浩斯遗忘规律和马太效应现象,建立了基于遗忘函数和旅游产品流行度的个性化旅游网络广告推荐模型,并提出三种改进流行度的方法,深入剖析流行度对推荐的影响,并结合相关案例进行数据仿真。研究结论表明,同时考虑遗忘函数和产品流行度,及改进的流行度模型比单一角度优化的模型预测精准度高,且单一角度优化的预测模型推荐精准度均高于传统协同过滤算法。优化的广告推荐方法不仅消除在线游客兴趣变化和产品流行度对推荐精准度的影响,同时缓解了网站信息量过载和降低了游客浏览的时间成本,为网站的精准化广告推荐提供一定的方法和手段,拓宽了旅游个性化推荐的研究思路。
[Abstract]:Tourism online advertising is one of the key means to speed up the information process of tourism, and tourism personalized information service is an important way to improve the effect of Internet advertising. However, the increasing number of tourism products, the accumulation of tourism plans and the increasing number of online tourists will not only take time for online tourists to find a long time to meet themselves. The content of the demand also leads to the overload of the tourist website information, which causes the increase of the time cost of the online tourists and the decline of the website advertising recommendation efficiency. In view of this, this paper puts forward the corresponding personalized recommendation optimization technology based on the Ebbinghaus's forgetting law and the Matthew effect, aiming at improving the accuracy of the recommended. First, the change of the interest of tourists. On the basis of Ebbinghaus's forgetting law, the interest forgetting function is integrated into the collaborative filtering algorithm, and the weight of tourists' scoring time is given to weaken the importance of the historical score to strengthen the importance of the current score. Then, the influence of the Matthew effect on the tourist interest pretest is analyzed, and the popularity of the tourist product is introduced into the synergy. The filtering algorithm increases the penalty value for popular products and reduces the influence of popularity on the similarity of tourists' interest. Finally, based on the influence of tourist history score information and product implied information on the tourist interest prediction, combining the Ebbinghaus forgetting law and the Matthew effect, it builds a forgetting function and the popularity of tourism products. The recommendation model of sexual tourism network advertising, and three ways to improve the popularity, in-depth analysis of the impact of popularity on recommendation, and the combination of related cases for data simulation. The conclusion shows that the forgetting function and product popularity are considered at the same time, and the improved popularity model is more accurate than the single angle optimization model. The single angle optimization prediction model recommends that accuracy is higher than the traditional collaborative filtering algorithm. The optimized advertising recommendation method not only eliminates the influence of online tourist interest changes and product popularity on the recommended accuracy, but also relieves the amount of information overload and reduces the time cost of visitors' browsing, and promotes the accurate advertising of the website. Recommendation provides certain methods and means to broaden the research idea of personalized tourism recommendation.
【学位授予单位】：湖南工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F592;F713.8

【参考文献】