Augmenting recommendation systems using a model of semantically-related terms extracted from user behavior
This addresses accuracy issues in recommender systems for users with sparse data, but it is incremental as it builds on existing keyword extraction methods.
The authors tackled the cold-start problem and limited user interactions in recommender systems by proposing a system that extracts semantically-related search keywords from aggregate user behavior, which improved recommendation accuracy.
Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender systems (RS). To overcome these challenges and many similar ones that result in low accuracy (precision and recall) recommendations, we propose a novel system that extracts semantically-related search keywords based on the aggregate behavioral data of many users. These semantically-related search keywords can be used to substantially increase the amount of knowledge about a specific user's interests based upon even a few searches and thus improve the accuracy of the RS. The proposed system is capable of mining aggregate user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free. These semantically related keywords are obtained by looking at the links between queries of similar users which, we believe, represent a largely untapped source for discovering latent semantic relationships between search terms.