Abstract: Aiming at the problem that the traditional context-aware recommendation algorithm is not accurate and the applicable environment is limited,this paper proposed a feasible solution.It can find relevant media content based on the detected context information,which is more effective than relying solely on feature extraction.First,the context data and the search information are used to identify a hidden relationship between the selected context and the user’s interest in a particular context,and a recommendation model of the unknown ranking is constructed.The contextually perceived ra-ting of the user’s expected ranking score is then calculated by using the given contextual list.The user’s situation is used to participate in the selection of new items so that the detected situation helps to facilitate the search for related items.The optimization function is further used to maximize the average accuracy (MAP) of the result recommendation.The experimental results show that compared with the more advanced algorithms,the proposed method shows betterperformance than the traditional collaborative filtering algorithm,and the absolute error value is reduced by 1.8% and 1.2% respectively in the recommendation accuracy and recall.The rate is also superior to the two comparison methods.

Key words: Context awareness, Implicit interest, Personalized recommendation algorithm, Recent neighborhood recommendation