Research On The Personalized Recommendation Combining With Emotional Tendency Analysis

The emergence and popularization of the Internet gives users a lot of information, meeting the needs of information of users in the information age. However, with the rapid development of network, it brings substantial growth in online information, arising the problem of information overload.A relatively novel solution to the problem of information overload is to design and implement a personalized recommendation system. An information recommendation system, which is based on requirements and interests of users, recommends information and products which users are interested in to them. Personalized recommendation systems have already been widely used in many fields.In order to better achieve personalized recommendation, this research will attempt to analyze users’ state of real feelings, carrying out users’ text emotion analysis.This research discovers advantages and disadvantages by comparing this algorithm with traditional recommendation algorithms. Then it quantifies the results of users’ text emotion analysis, which will be used in rating matrix and similarity computation, to improve recommendation algorithm and enhance effectiveness of recommendations. The main work of this article is described below.1. Seldom does traditional recommendation algorithms realize the importance of users’ real feelings. Considering this fact, an improved recommendation algorithm considering emotion analysis, on the basis of traditional collaborative filtering algorithm, is put forward, aiming at the issue of enhancing recommendation performance of recommendation system urgently.2. As the realization process of this algorithm involves a large number of text emotional tendency analysis, this research constructs hotel field sentiment dictionary on the basis of hotel field corpus and basic sentiment dictionary.3. For the purpose of fulfill hotel modeling and users’ emotion modeling, it improves traditional text feature selection algorithm and selects proper clustering algorithm combined with actual situation of hotel text, accomplishing designation of emotion value calculation formula.4. The structure of recommendation system, including emotion analysis module,user modeling module, object modeling module, recommendation algorithm module and so on, is to be designed, according to the designed recommendation algorithm.Using data set which has been grasped, top-N recommendation on the system which has been designed out is fulfilled.5. Compared with some previous recommendation algorithm on recall, accuracy and other indices by experiments, recommendation algorithm of this subject has been analyzed and compared with traditional recommendation algorithm on their differences of performances and effects.As the experiment shows, compared with traditional recommendation algorithm,the recommendation algorithm combining with emotional tendency analysis has improvement to some extent on the indices such as recall, accuracy and so on. Furthermore, analysis of text emotion and selection of cluster number can affect the whole performance of recommendation system, impacting on the results of personalized recommendation.