Name
Abstract | ||
|---|---|---|
Consuming behaviors of users form sequences ordered by time intuitively. Long Short-Term Memory Based Recurrent Neural Networks(LSTM), which are special kind of Recurrent Neural Networks, are ideal for modeling sequences. In this work, we propose a LSTM based model called CF-LSTM which can model the consuming sequences of users for Collaborative Filtering(CF). To effectively train the CF-LSTM model, we propose the step-combine technique, which processes k ratings at a time step and solves the long sequences problem of ratings. To improve the performance of CF-LSTM, we extend our model with ordinal cost by considering the ordinary nature of users' ratings. Finally, we compare our model with state-of-the-art methods in the metrics of accuracy, novelty and diversity. Extensive experiment results show that CF-LSTM provides highly accurate, novel and diverse recommendations, which outperforms state-of-the-art methods. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) | Collaborative filtering,Ordinal number,Computer science,Filter (signal processing),Long short term memory,Recurrent neural network,Artificial intelligence,Novelty,Machine learning,Distributed computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lixin Zou | 1 | 39 | 4.81 |
| Yulong Gu | 2 | 8 | 3.85 |
| Jiaxing Song | 3 | 50 | 9.62 |
| Weidong Liu | 4 | 93 | 17.66 |
| Yuan Yao | 5 | 591 | 53.27 |