Research On Context Aware Recommendation System In Mobile Environment

With the rapid development of information technology,the information on the Internet is increasing rapidly,and the growth of this kind of information brings some negative problems,such as the problem of information overload.How to solve the problem of information overload has become an important issue of academic research.In this case,the recommendation algorithm as an effective means of information filtering can effectively solve the problem of user information overload.Therefore,it has got more and more attention among scholars.However,most of the research is only concerned about how to recommend the most relevant items to the user,but ignore the relevant contextual information,such as time,place,or whether accompanied by others.The researchers and practitioners in field of e-commerce,information retrieval,mobile computing,data mining,marketing and management have begun to realize the importance of context information.Context information,which has certain influence in the recommendation system,is an important reference to provide recommendations.Due to different impact of situational factors,in this study,the calculation of weight proportion on the various situational factors is also needed.In conclusion,this paper comes up with a personalized recommendation model based on context awareness.To validate the validity and accuracy of the model,this paper will compare the improved model with the traditional collaborative filtering model.According to the status of the research context-aware recommendation,this paper,which is based on the recommendation of the movie ticket,takes the associative information as context-aware attributes.The associative information includes the time and location of film.First,calculate the weight of all the context attributes and the influence value of the context attributes on the recommended resources.Then,find similar users and calculate the similarity value of collaborative filtering based on the ratings from similar users.At last,recommend the list of information resources to the targeted user.Based on the above description,the main work of this paper is as follows:First of all,this paper studies the context aware recommendation system,the existing collaborative filtering algorithm based on users,summarizes the advantages and disadvantages of the proposed system,select the days(weekdays or weekend),time(day or evening)and location situation(close to home or not)as the situation attribute research according to the target user's consideration.Secondly,a personalized recommendation model based on context awareness is proposed;choose the Cinema ticket as the research object,and the days,time and location as the contextual variables.Calculate the weight of the three properties,respectively,and concluded the importance of those context and situational properties when choosing the movies.Distinguish importance between three context properties.Recommend personalized movies to target users in the mobile environment combining with the traditional collaborative filtering method.Experiment shows that personalized recommendation algorithm based on context aware has higher efficiency and effectiveness and can provide users with more context based recommendation services.Finally,the experimental study is carried out by choosing the MovieLens as data set,mean absolute error(MAE)as evaluation index of accuracy.By comparing the proposed personalized recommendation algorithm based on context aware and the user based collaborative filtering algorithm,the results show that the algorithm proposed in this paper is more accurate.