# Cross-Modal Manifold Propagation for Image Recommendation
## 4 Experimental Analysis  
This section conducts experiments and the corresponding analysis to show the capability of the proposed CMP in fusing advantages of both user interest and visual semantics for image recommendation. A social database was collected from social image sharing platform Huaban ([[http://huaban.com/](http://huaban.com/)]([http://huaban.com/](http://huaban.com/)), accessed on 15 January 2022), which contains actual records of user-image correspondence composed of 33,926 images and 4737 users with 1,610,984 feedback instances of sparsity 1.0%. User-image interactions are split into half for training and half for random testing. In the scenario of image recommendation, manifold propagation in the proposed CMP builds graphs with all users and images, on which only given interactions for training are available to learn pairwise user-image correlations \(F_{u}\) and \(F_{v}\) for recommendations. The following experiments were conducted to evaluate the effectiveness of CMP.  
* As a primary component in CMP, the manifold construction of user interests and visual semantics is explored first to investigate their complementary role in image recommendation.
* On collaborative fusion, experiments investigate multiple fusion rules compared with the introduced interest-aware semantic fusion to illustrate its merit.
* The performance of CMP is compared with that of single-modal UMP and VMP to illustrate the cross-modal collaborative ability of user manifold propagation and visual manifold propagation in CMP.
* Experiments were conducted to compare the recommendation performance of CMP to that of network-based inference (NBI) [14], collaborative filtering-based (CF) recommendation [40], content-based (CB) recommendation [11], content-based bipartite graph (CBG) [41], collaborative representation based inference (CRC) [42], SVM based inference [43], hybrid recommendation [21], progressive manifold ranking (PMR), and modularized manifold ranking (MMR) [23].  
_Precision, mean average precision (MAP)_, and _mean reciprocal rank (MRR)_ act to quantitatively verify performance for image recommendation. _Precision_ evaluates the percentage of correctly recommended images in top-N image recommendation. Higher _precision_ indicates more correct recommended images to users' personalized preferences and better performance. _MAP_ evaluates the mean of average precision on the ranking order of each correctly recommended image, which measures recommendation performance by considering the orders of recommended images. Higher _MAP_ implies better performance in which correct images take front rankings in recommendations. _MRR_ assesses the reciprocal of the recommended order of users' first correct recommended image, and adopts averages of all users as performance measurement. Higher _MRR_ indicates better performance with correct recommendations ranked in front. We carefully implemented the proposed CMP and its comparisons with the same experimental settings to report results on the same datasets for a fair comparison. All experiments were conducted on a PC with 3.6 GHz CPU and 32 G RAM running a Linux environment and MATLAB R2013a.