36 Cross Domain Recommender Systems: A Systematic Literature Review MUHAMMAD MURAD KHAN and ROLIANA IBRAHIM, Faculty of Computing, Universiti Teknologi Malaysia IMRAN GHANI, School of Information Technology, Monash University Malaysia Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. The objective of this research is to identify the most widely used CDRS building-block definitions, identify common features between them, classify current research in the frame of identified definitions, group together research with respect to algorithm types, present existing problems, and recommend future directions for CDRS research. To achieve this objective, we have conducted a systematic literature review of 94 shortlisted studies. We classified the selected studies using the tag-based approach and designed classification grids. Using classification grids, it was found that the category-domain contributed a maximum of 62%, whereas the time domain contributed at least 3%. User-item overlaps were found to have equal contribution. Single target domain recommendation task was found at a maximum of 78%, whereas cross-domain recommendation task had a minor influence at only 10%. MovieLens contributed the most at 22%, whereas Yahoo-music provided 1% between 29 datasets. Factorization-based algorithms contributed a total of 37%, whereas semantics-based algorithms contributed 6% among seven types of identified algorithm groups. Finally, future directions were grouped into five categories. Categories and Subject Descriptors: H.5.5 [Information Systems - information retrieval]: Recommender Systems General Terms: Survey, comparison, trend Additional Key Words and Phrases: Cross domain recommender systems, systematic literature review, cross domain transfer learning, multi domain recommender systems ACM Reference Format: Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. 2017. Cross domain recommender systems: A systematic literature Review. ACM Comput. Surv. 50, 3, Article 36 (June 2017), 34 pages. DOI: http://dx.doi.org/10.1145/3073565 1. INTRODUCTION Recommender systems are special software programs designed to recommend items to users based on their observed interest [Ricci et al. 2011]. A user’s interest with respect to recommended items is stored in the form of interaction, for example, numerical rating, inside a rating matrix. Therefore, users, items, and the rating matrix create a recommender systems ecosystem known as a domain. These days, recommender systems focus on item recommendation to a single domain. For example, AMAZON recommends items for sale to its interested users; Netflix Authors’ addresses: M. M. Khan and R. Ibrahim, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia; emails: {muradtariq.tk, drroliana.utm}@gmail.com; I. Ghani, School of Information Technology, Monash University Malaysia, Selangor Darul Ehsan, Malaysia; email: imransaieen@gmail.com. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. c  2017 ACM 0360-0300/2017/06-ART36 $15.00 DOI: http://dx.doi.org/10.1145/3073565 ACM Computing Surveys, Vol. 50, No. 3, Article 36, Publication date: June 2017.