Cross-Lingual Product Recommendation Using Collaborative Filtering with Translation Pairs

Abstract

We developed a cross-lingual recommender system using collaborative filtering with English-Japanese translation pairs of product names to help non-Japanese buyers visiting Japanese shopping Web sites who speak English. The customer buying histories at an English shopping site and those at another Japanese shopping site were used for the experiments. Two kinds of experiments were conducted to evaluate the system. They were (1) two-fold cross validation where the half of the translation pairs was masked and (2) experiments where the whole of the translation pairs were used. The precisions, recalls, and mean reciprocal rank (MRR) of the system were evaluated to assess the general performance of the recommender system in the former experiment. On the other hand, what kinds of items were recommended in more realistic scenario was shown in the later experiments. The experiments revealed that masked items were found more efficiently than bestseller recommender system and showed that items only at the Japanese site that seemed to be related to buyers’ interests could be found by the system in more realistic scenario.