TV Program Recommendation for Multiple Viewers Based on user Profile Merging

By Zhiwen Yu, Xingshe Zhou, Jianhua Gu. User modeling and user-adapted interaction, 2006

402 citations recorded by the source corpus.

How researchers see this paper

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Recommendation to Groups (2007, background): “But often the group members will expect a system to make recommendations concerning a larger set of decisions, either at the same time or in succession: I NTRIGUE ’s tour guide will choose several sights to visit; recommender will recommend several programs for a group to watch in succession.”
Recommender systems survey (2013, background): “RS that consider groups of users [108] are starting to expand and to be used in different areas: tourism [14], music [55], TV [238], web [176].”
Group Recommender Systems: Combining Individual Models (2011, background): “In contrast, some evaluation of YU'S TV RECOMMENDER has taken place [29].”
Group recommendations with rank aggregation and collaborative filtering (2010, background): “consists of creating a joint user profile for all the users in the group and then performing a recommendation for this artificial user represented by the group profile [14, 21].”
An Efficient Group Recommendation Model With Multiattention-Based Neural Networks (2020, neutral): “[15] proposed an efficient group recommendation approach.”
Recommender Systems (2016, neutral): “Simple averaging strategies do not work well when groups are heterogeneous and contain users with diverse tastes [653].”