  With the arrival of the big data era, recommendation system has been a hot
technology for enterprises to streamline their sales. Recommendation algorithms
for individual users have been extensively studied over the past decade. Most
existing recommendation systems also focus on individual user recommendations,
however in many daily activities, items are recommended to the groups not one
person. As an effective means to solve the problem of group recommendation
problem,we extend the single user recommendation to group recommendation.
Specifically we propose a novel approach for family-based shopping
recommendation system. We use the dataset from the real shopping mall
consisting of shopping records table, client-profile table and family
relationship table. Our algorithm integrates user behavior similarity and user
profile similarity to build the user based collaborative filtering model. We
evaluate our approach on a real-world shopping mall dataset.
