# Cross-Modal Manifold Propagation for Image Recommendation
## 4 Experimental Analysis
### Collaborative Fusion  
**Candidate size in cross-modal fusion:** CMP propagates user records along user interest manifold by UMP and generates a user interest-aware candidate set for reranking based on visual-view scores from VMP. The size of the candidate set affects recommendation performance. Therefore, a suitable candidate size is important for leveraging the advantages of both the views of user interest and visual semantics. Figure 5 presents the _precision_ of recommendation with different candidate sizes in CMP. CMP performed better when almost 1500 images are selected as candidates for recommendation. This implies that the trade-off between interest and visual semantics in recommending candidates lies in 1500 images. Fewer candidates cannot guarantee the scope of user interests. More candidates would result in similar contents of users' historical records to jam in front of the recommendation list with high possibility. Both the above cases would significantly affect users' communication experience. Therefore, CMP takes the trade-off of 1500 candidates for cross-modal image recommendation. Figure 4: _Precision_ of top-N recommendation of image manifold propagation with user-image relationship-based graph and visual representation-based graph. **Cross-modal fusion rules:** To verify the fusion strategy, a recommendation list is generated with local visual propagation within the candidate set of UMP, i.e., VMP is only conducted on a candidate set of UMP called UMIM. We also performed local UMP recommendations on the candidate set of VMP called IMUM. Experiments compared the designed **interest-aware visual fusion** in CMP with UMIM, IMUM, and min, max, and are pooling of user-image correlations estimated by both UMP and VMP modules. Figure 6 shows the recommended _precision_ of the proposed CMP compared with different fusion strategies. CMP performed better than the other strategies. Compared with UMIM and IMUM, CMP considers global distributions of both user interests and visual semantics with higher _precision_. At the same time, UMIM and IMUM take one global and the other local distributions. It means the proposed interest-aware visual fusion in CMP successfully leverages the merits of both UMP and VMP in estimating user-image correlation. It demonstrates that the proposed interest-aware visual fusion strategy fits the recommendation requirements better than its comparisons. **Cross-modal vs. single-modal:** As analyzed in Sections 4.1 and 4.2, the UMP module constructs recommendations on user-image correspondence, while the VMP module builds that on semantic visual features. They employ different data sources to investigate  
Figure 5: Effect of different candidate sizes in CMP by _precision_ of Top-N recommendation. Figure 6: _Precision_ of Top-N recommendation for the proposed CMP with different fusion strategies of UMIM, IMUM, and min, max, and ave pooling. user interest and visual distributions, respectively. The two modules in CMP intuitively compensate each other to some degree. To verify the collaborative capability of UMP and VMP in the proposed CMP framework, an ablation study was performed on them. Figure 7 presents the _precision_ and _MAP_ of top-N recommendation for the proposed cross-modal CMP as compared with single-modal UMP and VMP. Results indicate that CMP consistently performed better than VMP regarding both _precision_ and _MAP_, and CMP achieved better _MAP_ than that of UMP. On the first 6 recommended images, CMP recommended more correct images than UMP did, while the remaining ranked images recommended by UMP outperformed the others regarding _precision_. CMP performed better in _MAP_ than in _precision_, which means that CMP recommended correct images in the front order of recommendation sequence, as the order is greatly important in recommendation. Global evaluation _MRR_ is further provided on the order of correct recommendation of CMP in Table 1. This shows that the proposed CMP achieved 45% in _MRR_, which was significantly higher than the 26.5% of UMP and 32% of VMP.