基于Web日志和协同过滤算法的水产养殖信息推荐

随着水产养殖信息在互联网和物联网中快速增长,大部分信息成为死信息储存于互联网中各个角落,未得到有效利用。为了从众多资源候选中选择用户需要的资源推送给用户,该文构建了一个基于Web日志的水产养殖推荐系统。该系统通过用户兴趣调查获取用户初始倾向,并运用Web日志挖掘技术构建了潜在的用户兴趣模型,将用户个性化的信息通过基于项目的协同过滤算法整合至推荐系统。基于用户的推送系统实现根据用户的需求精确推荐水产品交易、养殖技术、政府优惠政策和物联网数据等信息。

Aquaculture information recommendation based on collaborative filtering algorithm and web logs

Abstract: With the development of Internet, aquaculture information is increasing rapidly in decade years in both internet and the Internet of Things (IoT). Now users are confronted with the information overload, and most of the information has not been effectively utilized. In order to select requested resource for specific users from ubiquitous resource, an aquaculture recommender system was built in this paper. Several IoTs had been deployed in many provinces of China, like Beijing, Jiangsu and Shandong. What''s more, an IoT platform was established to collect IoT environment information in real time and crawl aquaculture information from Internet. In this research, item based collaborative filtering algorithm was combined with Web usage mining to generate recommendation. Web usage mining technology processed Web logs in host server and analyzed browsing behavior of users to establish user preference model. Furthermore, item based collaborative filtering used collective intelligence to recommend items that was similar with the items user prefers. The producers of this research were as follows. First, an aquaculture household interest questionnaire was designed after field research with aquaculture households. The questionnaire included user basic investigation, pond basic investigation and user interest investigation. Second, users rated the items in the questionnaire according to their interests. These items included aquaculture technology, fish disease prevention knowledge, government policies, pond production information, disease warning information, weather information, information output prediction and fault information etc. So that user tendency was initialized through the questionnaire and system registration. Third, further user interest could be collected in the form of Web logs. Web logs recorded user browsing behavior, such as user name, IP, access date and time and visited pages. User browsing behavior indicated user interests. To a certain extent, the longer time user browsers a page, the more interest user have. This technology was called Web usage mining. After user initial model and Web usage mining, the user-item rating matrix was calculated. Fourth, item-based collaborative filtering calculated the similarity between two items. The methods of similarity calculation mainly included Pearson similarity, cosine similarity, general modified cosine similarity and improved modified cosine similarity. Considering different user rating behaviors, some users would rate much higher or lower score than the average. So improved modified cosine similarity method was used to reduce the impact of user rating behavior. Using this method, all ratings of one user would be divided by highest rating, and the new user-item rating matrix was obtained. Fifth, the prediction rating from users to items was produced based on item similarity. The recommendation system pushed the items with highest prediction rating to the corresponding users using the top neighborhood method. Finally, this research was evaluated by mean absolute error. Results showed that when recommend items were more than 4, MAE of improved modified cosine method were the least in all methods. So that, the improved modified cosine method (IMCM) was chosen to calculate item similarity. In conclusion, the cold start problem of recommender system could be solved by initial user interest like user registration and questionnaire investigation. And the IMCM method improves the accuracy by reducing impact of user behavior. The aquaculture system recommends precisely according to user interest from both Internet and Internet of Things (IoT), such as trading information, farming technology, government policies and Internet of Things data. There are further works to do in the future. Context aware can be taken into consideration to improve recommendation precision, such as time, location and fault information in pond. Time presents the different production season of aquatic products; location presents where the pond is; and the fault information presents the abnormal status of IoT in pond.