林业特色产品推荐系统设计
文献综述
摘要：随着网络不断的普及，网络资源日益丰富，网络信息量逐渐膨胀。用户要在众多的选择中挑选出自己真正需要的信息好比大海捞针，出现了所谓的“信息过载”的现象。信息过载是指的是社会信息超过了个人或系统所能接受、处理或有效利用的范围，并导致故障的状况。个性化推荐系统的出现是为了解决信息过载的问题，帮助消费者在浩如烟海的产品中找到自己需要的产品，为消费者提供个性化的购物体验。个性化推荐系统日益受到用户的青睐，也受到越来越多的学者和电子商务网站的关注。
个性化推荐可以作为网络营销的一种手段，能为电子商务网站带来巨大的利益。个性化推荐的目标是根据具有相似偏好的用户的观点向目标用户推荐新的商品。好的个性化推荐系统能够发掘用户喜欢的商品，并推荐给用户。可想而知，对于用户而言，如果打开网站的链接并登陆，就能找到自己喜欢的商品，那会省下很多翻看网页的时间和精力，而这样的网站，一定会受到用户的青睐。一个好的个性化推荐系统可以为用户提供便利，继而，使用户与网站之间有更好的粘合度，提高电子商务网站的市场竞争能力。
在众多的个性化推荐算法中，协同过滤被广泛应用，也是最成功的推荐算法。本课题旨在研究基于协同过滤推荐算法在林业产品电子商务个性化商品推荐中的应用。林业特色产品作为促进地方林业经济发展的动力之一，就更加需要一个优秀的林业特色产品推荐系统来发挥自己的功能，例如利用推荐产品来增加产品销售数量，出售更多品类的产品，增加用户满意度和忠诚度，同时推荐系统也能更好地了解用户需求，收集的数据也方便管理员做出更好的改进。
关键词：林业特色产品电子商务 推荐系统 协同过滤
THEME：Design of forestry characteristic product recommendation system
Abstract: With the continuous popularization of the network, the network resources are increasingly rich, and the amount of network information is gradually expanding. Users have to choose the information they really need from many choices, just like looking for a needle in a haystack, which leads to the so-called 'information overload' phenomenon. Information overload refers to the situation that social information exceeds the range that individuals or systems can accept, process or effectively use, and leads to failure. The emergence of personalized recommendation system is to solve the problem of information overload, help consumers find the products they need in the vast number of products, and provide consumers with personalized shopping experience. Personalized recommendation system is becoming more and more popular among users, as well as more and more scholars and e-commerce websites.
Personalized recommendation can be used as a means of network marketing, which can bring huge benefits to e-commerce websites. The goal of personalized recommendation is to recommend new products to target users according to the views of users with similar preferences. Personalized products can be recommended to users. It can be imagined that for users, if you open the link of the website and log in, you can find the goods you like, which will save a lot of time and energy to browse the web page, and such a website will be favored by users. A good personalized recommendation system can provide convenience for users, and then, make users and websites have better adhesion, improve the market competitiveness of e-commerce websites.
Among many personalized recommendation algorithms, collaborative filtering is widely used and the most successful recommendation algorithm. This paper aims to study the application of Collaborative Filtering Recommendation Algorithm in the personalized product recommendation of forestry products e-commerce. As one of the driving forces to promote the development of local forestry economy, forestry characteristic products need an excellent recommendation system for forestry featured products to play their own functions. For example, using recommended products to increase the sales quantity of products, selling more products of different categories, increasing user satisfaction and loyalty, and at the same time, the recommendation system can better understand the user needs and collect data It is also convenient for administrators to make better improvements.
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