Adaptive collaborative topic modeling for online recommendation

Collaborative filtering (CF) mainly suffers from rating sparsity and from the cold-start problem. Auxiliary information like texts and images has been leveraged to alleviate these problems, resulting in hybrid recommender systems (RS). Due to the abundance of data continuously generated in real-world applications, it has become essential to design online RS that are able to handle user feedback and the availability of new items in real-time. These systems are also required to adapt to drifts when a change in the data distribution is detected. In this paper, we propose an adaptive collaborative topic modeling approach, CoAWILDA, as a hybrid system relying on adaptive online Latent Dirichlet Allocation (AWILDA) to model newly available items arriving as a document stream and incremental matrix factorization for CF. The topic model is maintained up-to-date in an online fashion and is retrained in batch when a drift is detected using documents automatically selected by an adaptive windowing technique. Our experiments on real-world datasets prove the effectiveness of our approach for online recommendation.