The emergence of new TV media such as digital cable and satellite has diversified TV contents. This makes the task of finding one's favorite contents among hundreds of TV channels a time consuming job. To relieve this channel selection overhead, this paper presents personalized recommendation schemes for DTV channel selectors. The proposed recommendation schemes analyze each person's previous watching behavior in terms of recency and frequency, and then utilize this information in the control of the top-down channel selector. Simulation studies show that the proposed schemes reduce the seek cost of the DTV channel selector by up to 62.8% for the trace set we considered
['Hyejeong Lee', 'Soyoon Lee', 'Heekyung Kim', 'Hyokyung Bahn']
Personalized recommendation schemes for DTV channel selectors