Integrating Topic Models and Latent Factors for Recommendation

TL;DR

This paper enhances hotel recommendation systems by integrating location data and user preferences to provide dynamic, trip-level personalization, addressing limitations of static user modeling in traditional collaborative filtering methods.

Contribution

It introduces a novel approach that combines location information with latent factor models to improve personalized hotel recommendations for travel planning.

Findings

Improved recommendation accuracy with location-aware models

Demonstrated effectiveness in trip-level personalization

Enhanced user satisfaction in hotel recommendations

Abstract

Nowadays, we have large amounts of online items in various web-based applications, which makes it an important task to build effective personalized recommender systems so as to save users' efforts in information seeking. One of the most extensively and successfully used methods for personalized recommendation is the Collaborative Filtering (CF) technique, which makes recommendation based on users' historical choices as well as those of the others'. The most popular CF method, like Latent Factor Model (LFM), is to model how users evaluate items by understanding the hidden dimension or factors of their opinions. How to model these hidden factors is key to improve the performance of recommender system. In this work, we consider the problem of hotel recommendation for travel planning services by integrating the location information and the user's preference for recommendation. The intuition is that user preferences may change dynamically over different locations, thus treating the historical decisions of a user as static or universally applicable can be infeasible in real-world applications. For example, users may prefer chain brand hotels with standard configurations when traveling for business, while they may prefer unique local hotels when traveling for entertainment. In this paper, we aim to provide trip-level personalization for users in recommendation.