Joint Factorizational Topic Models for Cross-City Recommendation

The research of personalized recommendation techniques today has mostly parted into two mainstream directions, namely, the factorization-based approaches and topic models. Practically, they aim to benefit from the numerical ratings and textual reviews, correspondingly, which compose two major information sources in various real-world systems, including Amazon, Yelp, eBay, Netflix, and many others. However, although the two approaches are supposed to be correlated for their same goal of accurate recommendation, there still lacks a clear theoretical understanding of how their objective functions can be mathematically bridged to leverage the numerical ratings and textual reviews collectively, and why such a bridge is intuitively reasonable to match up their learning procedures for the rating prediction and top-N recommendation tasks, respectively. In this work, we exposit with mathematical analysis that, the vector level randomization functions to harmonize the optimization objectives of factorizational and topic models unfortunately do not exist at all, although they are usually pre-assumed and intuitively designed in the literature. Fortunately, we also point out that one can simply avoid the seeking of such a randomization function by optimizing a Joint Factorizational Topic (JFT) model directly. We further apply our JFT model to the cross-city Point of Interest (POI) recommendation tasks for performance validation, which is an extremely difficult task for its inherent cold-start nature. Experimental results on real-world datasets verified the appealing performance of our approach against previous methods with pre assumed randomization functions in terms of both rating prediction and top-N recommendation tasks.