Recommendation on Flickr by combining community user ratings and item importance
Yuchen Jing, Xiuzhen Zhang, Lifang Wu, Jinqiao Wang, Zemeng Feng, Dan Wang · 2014
Photo recommendation in photo-sharing social networks like Flickr is an important problem. Collaborative filtering is very popular, which assumes each item has the same weight for recommendation. In practice some items are representatives for a class of items and therefore are more important for recommendation. In this paper, we model the importance for items by examining sentiment from the general public towards items. Specifically we propose a model using the temporal dynamic user `favor' information to infer photo importance on Flickr. It is further combined with local community user ratings to improve the Probabilistic Matrix Factorization (PMF) framework for photo recommendation. Experiment results show the effectiveness of the proposed approach.