Abstract: Abstract: In view of the problems in the existing course recommendation system， such as over-reliance on users' grades of courses， failure to consider the change of users' interests over time and neglect the collocation between the courses learned by users and the recommended courses， a TIMR course recommendation model based on the degree of time correlation and course collocation is proposed. On the one hand， TIMR model uses course viewing progress instead of course rating， and applies time correlation function to calculate the similarity between courses. On the other hand， the course collocation degree function is constructed by using the co-selected frequency of the course. Then， time correlation and course collocation are combined to produce predictive grades. In order to verify the validity of TIMR model， experiments are conducted on TM data set， CN data set and MOOC data set. Experiments show that compared with the existing recommendation methods UserCF， ItemCF， LFM， PR， MPR and SMCR， TIMR significantly improves the Precision， Recall and F1_score indexes， which has obvious advantages in improving the recommendation quality.

Key words: Key words: course grading, watch progress, time relevance, course correlation, course recommendation