With the increase of TV channels

the application of recommender systems in the field of live TV has become a research hotspot. However

a traditional VOD (Video on Demand) recommender system is unable to be directly applied in live TV because of its special way of broadcasting and watching

and the existing methods of recommending channels do not pay attention to status of TV shows being broadcasted

which affects recommendation accuracy

and the methods of recommending programs are difficult to handle cold start of TV shows. Therefore

this paper proposes a rating prediction al

gorithm by fusing TV channel recommendation method and TV program recommendation method OFAP (Over the First by Adding Preference). Firstly

we construct different channel-time preference matrix and rating weight matrix of pre-recommendation for each user by clustering their viewing logs. Secondly

we propose a rating strategy to alleviate the cold-start problem of TV programs for existing program recommendation algorithms

and we adopt one of them to perform pre-recommendation. Finally

we combine user's preference

rating weight and rating of pre-recommendation to construct a prediction function

which is trained with the results of pre-recommendation. Experiments on industrial datasets show that the proposed model OFAP significantly outperforms baseline algorithms when Precision@

N

and Recall@

N

are adopted as criterias.