Recommendation of TV programs via information filtering in RCA tripartite networks

Abstract

Television remains an indispensable medium for information and entertainment, even in the era of widespread streaming media. With the expansion of TV channels through set-top boxes, users now face an overwhelming variety of choices, leading to information overload problems. Recommendation systems have effectively solved the information overload problem and can thus be naturally applied to television. Prior research has focused on improvements in algorithms and the addition of other data. In this paper, without introducing external data, we generate recommendations based on 3 months of TV viewing data from a Chinese city. Considering the large amount of noisy data caused by short stays in TV programs, we simplify the original almost fully connected tripartite network by eliminating the insignificant links with the Revealed Comparative Advantage (RCA) metric to comprehensively reflect user preferences. The inclusion of channel nodes allows the network structure to better align with user behavior characteristics, which differs from traditional bipartite networks that only include user-program interactions. We examine data with different sparsity and find that our approach continues to outperform conventional bipartite network recommendations in terms of accuracy. The advantages of our approach have been validated through comparisons with other advanced methods and across different datasets. Overall, only based on viewing records of users, our work provides accurate TV program recommendations that can capture the underlying user behavior characteristics.