2014/09/08 by Khalifeh AlJadda, Mohammed Korayem, AlJadda, Khalifeh +14
Computer Science · #Data Management and Algorithms #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Recommender Systems and Techniques #cs.IR
paper · pdf · doi:10.48550/arxiv.1409.2530
RecSys2014
arxiv created 2014/09/08 · openalex publication_date 2014/09/08 · arxiv updated 2014/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.