Joint factorizational topic models for cross-city recommendation

Abstract

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.