Family Shopping Recommendation System Using User Profile and Behavior Data

24 Aug 2017  ·  Jiacheng Xu ·

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.

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here