A Latent Topic Based Collaborative Filtering Recommendation Algorithm for Web Communities

Providing personalized high quality community recommendation for Web community members has become increasingly important. Traditional collaborative filtering methods based on explicit topic associations cannot solve the information sparsity problem. The recommendation methods based on latent topic association results in inaccurate results. To solve the above problems, we propose a collaborative Web community recommendation algorithm based on latent topic. Our algorithm generates the latent link between communities and members using latent topic associations to overcome the sparsity problem. Our algorithm also reduces inaccurate results by combining similar members' behaviors and interests. The experiment indicates that our recommendation algorithm has higher recommendation accuracy than traditional methods.