Product Recommendation Based on Search Keywords

Abstract

Recommender systems have been widely deployed on E-commerce websites. The cold start problem of making effective recommendations to new users without any historical data on the website is still challenging. These new users often have some available information, such as search keywords, before visiting the website. It is natural to use the information to predict users' preference, such that an immediate recommendation is possible. In this paper, we propose a new product recommendation approach for new users based on the implicit relationships between search keywords and products. The relationships between keywords and products are represented in a graph and relevance of keywords to products is derived from attributes of the graph. The relevance information will be utilized to predict preferences of new users. A preliminary experiment is conducted and shows that our approach outperforms the traditional approach (Recommending Most Popular Products).