Multi-Pointer Co-Attention Networks for Recommendation
Table 1. Performance comparison (mean squared error) on 24 benchmark datasets. The best performance is in boldface. ΔDC,ΔTN,ΔDA are the relative improvements (%) of MPCN over DeepCoNN (D-CON), TransNet (T-NET) and D-ATT respectively. MPCN achieves state-of-the-art performance, outperforming all existing methods on 24 benchmark datasets.
['Dataset', 'Interaction-based MF', 'Interaction-based FM', 'Interaction-based MLP', 'Interaction-based NEUMF', 'Review-based D-CON', 'Review-based TNET', 'Review-based D-ATT', 'Review-based MPCN', 'Improvement (%) Δ [ITALIC] DC', 'Improvement (%) Δ [ITALIC] TN', 'Improvement (%) Δ [ITALIC] DA']
Firstly, we observe that our proposed MPCN is the top performing model on all 24 benchmark datasets. This ascertains the effectiveness of our proposed model and clearly answers RQ1. MPCN consistently and significantly outperforms DeepCoNN, TransNet and D-ATT, which are all recent competitive review-based methods for recommendation. The relative improvement is also encouraging with gains of up to 71% (DeepCoNN), 19% (TransNet) and 5% (D-ATT). On majority of the datasets, performance gains are modest, seeing an improvement of 1%−3% for most models. Notably, the average percentage improvement of MPCN over DeepCoNN is 16%. The average performance gain over TransNet and D -ATT is a modest 3.2% and 2.2% respectively.