基于社交网络的用户需求发现与物品推荐

Users' Interests Exploration and Products Recommendation in Social Networks

微博作为当下最受欢迎的社交网络之一,包含了大量的用户需求和兴趣偏好信息,如何动态地从微博内容中提取用户的需求和偏好信息,将推荐算法结合社交网络产生推荐结果,解决信息过载的问题,目前暂时还没有相关的较为成熟的应用。本文设计并实现了基于社交网络的物品推荐系统,提取用户微博内容关键词作为用户需求特征,建立物品信息库,通过文本相似度计算用户需求和物品信息之间的匹配度,采用基于内容的推荐算法产生推荐结果。最后进行离线实验,对推荐系统产生的推荐结果进行评测分析。

Microblog is one of the most popular social networks, containing plenty of information of users' preference and needs. However, there are still no mature applications to extract the information of users' preference and needs from microblogs and combine those with recommendation algorithms to recommend items. Thus, this paper proposes and implements a recommendation system for social networks. First of all, users' content of weibo was crawled and keywords were extracted as characteristics of users' needs. Next, an item repository was built with specific characteristics of the items. Using the text similarity algorithm, similarity between users' needs and items' characteristics can be computed. Then with the content-based recommendation algorithm, we produced recommendation results for users which they may be interested in. At last four offline experiments on the recommendation results were done to evaluate and analyze the performance of this recommendation system.