Journal
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
Volume 2020, Issue 1, Pages -Publisher
SPRINGER
DOI: 10.1186/s13638-020-01716-2
Keywords
Multi-source heterogeneous data; Recommendation model; Social network
Funding
- National Key Research and Development Program of China [2018YFC0831903]
- Major Project of National Natural Science Foundation of China [51935002]
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Recommendation systems are often used to solve the problem of information overload on the Internet. Many types of data can be used for recommendations, and fusing different types of data can make recommendations more accurate. Most existing fusion recommendation models simply combine the recommendation results from different data instead of fully fusing multi-source heterogeneous data to make recommendations. Furthermore, users' choices are usually affected by their direct and even indirect friends' preferences. This paper proposes a hybrid recommendation model BRScS (an acronym for BPR-Review-Score-Social). It fully fuses social data, score, and review together; uses improved BPR model to optimize the ranking; and trains them in a joint representation learning framework to get the top-Nrecommendations. User trust model is used to introduce social relationships into the rating and review data, PV-DBOW model is used to process the review data, and fully connected neural network is used to process the rating data. Experiments on Yelp public dataset show that the BRScS algorithm proposed outperforms other recommendation algorithms such as BRSc, UserCF, and HRSc. The BRScS model is also scalable and can fuse new types of data easily.
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