A New Approach to Contextual Suggestions Based on Word2Vec
Yongqiang Chen, Tang Zhen-jun, Xiaozhao Zhao, Dawei Song, Peng Zhang · 2014
Abstract. We report our participation in the contextual suggestion track of TREC 2014 for which we submitted two runs using a novel ap-proach to complete the competition. The goal of the track is to generate suggestions that users might fond of given the history of users ’ prefer-ence where he or she used to live in when they travel to a new city. We tested our new approach in the dataset of ClueWeb12-CatB which has been pre-indexed by Luence. Our system represents all attractions and user contexts in the continuous vector space learnt by neural network language models, and then we learn the user-dependent profile model to predict the user’s ratings for the attraction’s websites using Softmax. Finally, we rank all the venues by using the generated model according the users ’ personal preference.