Total views : 693
Approaches, Issues and Challenges in Recommender Systems: A Systematic Review
Objectives: Today the recommendation technology has managed to achieve a distinct place in the modern and fascinating world of e-commerce applications as it helps the user in selecting items or products of his interest from a large pool. The present article aims to provide a comprehensive and systematic review of the state-of-the-art recommender systems. Methods/Statistical Analysis: The entire literature review process was divided into six research questions keeping in view the different perspectives of recommendation field. The methodology adopted here, consists of the search plan and the paper selection criteria. The search plan attempts to retrieve the research studies through several digital libraries and the paper selection criteria help filter out the most relevant studies further to gather evidence against each of the research questions. Findings: The literature review process provides a thorough discussion on different techniques deployed in recommender system literature such as collaborative filtering, content-based filtering, social filtering, demographic and knowledge-based and utility based systems. It also explores their strengths and weaknesses. The recommender systems face certain challenges in their deployment such as cold-start, sparsity, scalability, user privacy, etc. The different application domains where recommender systems are being adopted these days include movie, music, books, news, tourism etc. The gap analysis conducted during literature review, focuses on improving the traditional recommendation approaches, the precise blend of existing approaches with different types of information, modeling of user profiles and recommended items, standardization of non-standard evaluation techniques etc. Application/Improvements: This paper also throws some light on certain application fields such as television, research grants, restaurant, job search, etc. that need to grab the attention of scientific and research communities to promote more research in those areas.
Application Domains, Collaborative Filtering, Evaluation Measures, Recommendation Approaches, Recommender Systems.
- Liu J, Jiang Y, Li Z, Zhang X, Lu H. Domain-sensitive recommendation with user-item subgroup analysis. IEEE Transactions on Knowledge and Data Engineering. 2016 Apr; 28(4):939–50.
- Burke R. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction.2002 Nov, 12(4):331–370.
- Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. Springer US, 2011, pp. 1–35.
- Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005 Jun; 17(6):734–49.
- Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Advances in artificial intelligence. New York; 2009 Jan. 4:1–99.
- Najafabadi MK, Mahrin MN. A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artificial Intelligence Review. 2016 Feb; 45(2):167–201.
- Park DH, Kim HK, Choi IY, Kim JK. A literature review and classification of recommender systems research. Expert Systems with Applications. 2012 Sep, 39(11):10059–72.
- Park DH, Kim HK, Kim JK, Choi IY, Kim JK. A review and classification of recommender systems research. International Proceedings of Economics Development and Research. 2011; 5(1):290–4.
- Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T. Recommender systems. Physics Reports. 2012 Oct; 519(1):1–49.
- Véras D, Prota T, Bispo A, Prudêncio R, Ferraz C. A literature review of recommender systems in the television domain. Expert Systems with Applications. 2015 Dec; 42(22):9046–76.
- Felderer M, Fourneret E. A systematic classification of security regression testing approaches. International Journal on Software Tools for Technology Transfer. 2015 Jun; 17(3):305–19.
- Melville P, Sindhwani V. Recommender systems. Encyclopedia of machine learning. Springer US; 2011. p. 829–38.
- Elahi M, Ricci F, Rubens N. A survey of active learning in collaborative filtering recommender systems. Computer Science Review; 2016 May.
- Schafer JB, Frankowski D, Herlocker J, Sen S. Collaborative filtering recommender systems. The adaptive web. Springer Berlin Heidelberg; 2007. p. 291–324.
- Arazy O, Kumar N, Shapira B. Improving social recommender systems. IT Professional Magazine. 2009 Jul; 11(4):31–7.
- Sinha RR, Swearingen K. Comparing recommendations made by online systems and friends. DELOS workshop: Personalisation and Recommender Systems in Digital Libraries; 2001 Jun. p. 106.
- Mahmood T, Ricci F. Towards learning user-adaptive state models in a conversational recommender system. LWA; 2007. p. 373–8.
- Bridge D, Göker MH, McGinty L, Smyth B. Case-based recommender systems. The Knowledge Engineering Review. 2005 Sep; 20(03):315–20.
- Sharma R, Singh R. Evolution of recommender systems from ancient times to modern era: A survey. Indian Journal of Science and Technology. 2016 May; 9(20):1–12.
- Bobadilla J, Ortega F, Hernando A, Gutiérrez A. Recommender systems survey. Knowledge-Based Systems. 2013 Jul; 46:109–32.
- Sharma SK, Suman U. An efficient semantic clustering of URLs for web page recommendation. International Journal of Data Analysis Techniques and Strategies. 2013 Jan; 5(4):339–58.
- Ma H, Yang H, Lyu MR, King I. Sorec: Social Recommendation using probabilistic matrix factorization. Proceedings of the 17thACM Conference on Information and Knowledge Management; 2008 Oct. p. 931–40.
- Melville P, Mooney RJ, Nagarajan R. Content-boosted collaborative filtering for improved recommendations. In Proceeding Eighteenth national conference on Artificial intelligence; 2002 Jul. p. 187–92.
- Schein AI, Popescul A, Ungar LH, Pennock DM. Methods and metrics for cold-start recommendations. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; 2002 Aug. p. 253–60.
- Han P, Xie B, Yang F, Shen R. A scalable P2P recommender system based on distributed collaborative filtering. Expert Systems with Applications. 2004 Aug; 27(2):203–10.
- George T, Merugu S. A scalable collaborative filtering framework based on co-clustering. Proceedings of 5thIEEE International Conference on Data Mining. 2005 Nov; p. 625–8.
- Shyong K, Frankowski D, Riedl J. Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. Emerging Trends in Information and Communication Security. Springer Berlin Heidelberg; 2006. p. 14–29.
- Aghili G, Shajari M, Khadivi S, Morid MA. Using genre interest of users to detect profile injection attacks in movie recommender systems. Proceedings of 10thIEEE International Conference on Machine Learning and Applications (ICMLA); 2011 Dec. p. 49–52.
- Singh SP, Singh P. Design and implementation of a location-based multimedia mobile tourist guide system. International Journal of Information and Communication Technology. 2014 Dec; 7(1):40–51.
- Sae-Ueng S, Pinyapong S, Ogino A, Kato T. Personalized shopping assistance service at ubiquitous shop space. Proceedings of 22nd IEEE International Conference on Advanced Information Networking and Applications-Workshops (AINAW); 2008 Mar. p. 838–43.
- Schafer JB, Konstan JA, Riedl J. E-commerce recommendation applications. Applications of Data Mining to Electronic Commerce. Springer US; 2001. p. 115–53.
- Hwang CS, Kuo N, Yu P. Representative-based diversity retrieval. Proceedings of 3rd IEEE International Conference on Innovative Computing Information and Control (ICICIC); 2008 Jun. p. 155.
- Rao KN. Application domain and functional classification of recommender systems-a survey. DESIDOC Journal of Library and Information Technology. 2008 May; 28(3):17–35.
- Montaner M, López B, De La Rosa JL. A taxonomy of recommender agents on the internet. Artificial Intelligence Review. 2003 Jun; 19(4):285–330.
- Jelassi MN, Ben Yahia S, MephuNguifo E. A personalized recommender system based on users' information in folksonomies. Proceedings of 22nd International Conference on World Wide Web Companion; 2013 May. p. 1215–24.
- Gomez-Uribe CA, Hunt N. The netflix recommender system: algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS). 2016 Jan; 6(4).
- Mukherjee R, Jonsdottir G, Sen S, Sarathi P. MOVIES2GO: An online voting based movie recommender system. Proceedings of 5th ACM International Conference on Autonomous Agents; 2001 May. p. 114–15.
- Kompatsiaris Y, Merialdo B, Lian S. TV content analysis: Techniques and applications. CRC Press; 2012 Mar.
- Lops P, De Gemmis M, Semeraro G. Content-based recommender systems: State of the art and trends. Recommender Systems Handbook, Springer US; 2011. p. 73–105.
- Celma Ò, Ramírez M, Herrera P. Foafing the music: A music recommendation system based on RSS feeds and user preferences. International Society of Music Information Retrieval; 2005.
- Celma Ò, Serra X. FOAFing the Music: Bridging the semantic gap in music recommendation. Web Semantics: Science, Services and Agents on the World Wide Web. 2008; 6(4):250–6.
- Ahn JW, Brusilovsky P, Grady J, He D, Syn SY. Open user profiles for adaptive news systems: help or harm? Proceedings of 16th ACM International Conference on World Wide Web; 2007 May. p. 11–20.
- Billsus D, Pazzani MJ, Chen J. A learning agent for wireless news access. Proceedings of 5th ACM International Conference on Intelligent User Interfaces; 2002 Jan. p. 33–6.
- Borràs J, Moreno A, Valls A. Intelligent tourism recommender systems: A survey. Expert Systems with Applications. 2014 Nov; 41(16):7370–89.
- Sebastia L, Garcia I, Onaindia E, Guzman C. e-Tourism: a tourist recommendation and planning application. International Journal on Artificial Intelligence Tools. 2009 Oct; 18(5):717–38.
- Vansteenwegen P, Souffriau W, Berghe GV, Van Oudheusden D. The city trip planner: an expert system for tourists. Expert Systems with Applications. 2011 Jun; 38(6):6540–6.
- Montejo-Ráez A, Perea-Ortega JM, García-Cumbreras MÁ, Martínez-Santiago F. Otiŭm: A web based planner for tourism and leisure. Expert Systems with Applications. 2011 Aug; 38(8):10085–93.
- Ricci F, Nguyen QN, Averianova O. Exploiting a map-based interface in conversational recommender systems for mobile travelers. Tourism Informatics: Visual Travel Recommender Systems, Social Communities, and User Interface Design: IGI Global, Information Science Reference; 2009 Sep. p. 73–93.
- Santiago FM, López FA, Montejo-Ráez A, López AU. GeOasis: A knowledge-based geo-referenced tourist assistant. Expert Systems with Applications. 2012 Oct; 39(14):11737–45.
- Rey-López M, Barragáns-Martínez AB, Peleteiro A, Mikic-Fonte F, Burguillo JC. moreTourism: Mobile Recommendations for tourism. Proceedings of IEEE International Conference on Consumer Electronics (ICCE), Las Vegas (USA); 2011 Jan.
- Braunhofer M, Elahi M, Ricci F, Schievenin T. Context-aware points of interest suggestion with dynamic weather data management. Information and Communication Technologies in Tourism. Springer International Publishing; 2014. p. 87–100.
- Avancini H, Candela L, Straccia U. Recommenders in a personalized, collaborative digital library environment. Journal of Intelligent Information Systems. 2007 Jun; 28(3):253–83.
- Vera-del-Campo J, Pegueroles J, Hernández-Serrano J, Soriano M. DocCloud: A document recommender system on cloud computing with plausible deniability. Information Sciences. 2014 Feb; 258:387–402.
- Middleton SE, Shadbolt NR, De Roure DC. Ontological user profiling in recommender systems. ACM Transactions on Information Systems (TOIS). 2004 Jan; 22(1):54–88.
- Dron J, Mitchell R, Siviter P, Boyne C. CoFIND - an experiment in N-dimensional collabo-rative filtering. Journal of Network and Computer Applications. 2000 Apr; 23(2):131–42.
- Viappiani P, Pu P, Faltings B. Conversational recommenders with adaptive suggestions. Proceedings of ACM Conference on Recommender Systems; 2007 Oct. p. 89–96.
- Lee DH, Brusilovsky P. Fighting information overflow with personalized comprehensive information access: A proactive job recommender. Proceedings of 3rd IEEE International Conference on Autonomic and Autonomous Systems (ICAS07); 2007 Jun. p. 21.
- O’connor M, Cosley D, Konstan JA, Riedl J. PolyLens: A recommender system for groups of users. Proceedings of 7th European Conference on Computer-Supported Cooperative Work, Springer Netherlands; 2001 Sep. p. 199–218.
- Goldberg K, Roeder T, Gupta D, Perkins C. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval. 2001 Jul; 4(2):133–51.
- Park K. The potential knowledge recommendation system using User’s search logs. Indian Journal of Science and Technology. 2016 Jun; 9(24):1–7.
- Gunawardana A, Shani G. A survey of accuracy evaluation metrics of recommendation tasks. The Journal of Machine Learning Research. 2009 Dec; 10:2935–62.
- Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating collaborative filtering recomm-ender systems. ACM Transactions on Information Systems (TOIS). 2004 Jan; 22(1):5–53.
- Ferri C, Hernández-Orallo J, Modroiu R. An experimental comparison of performance measures for classification. Pattern Recognition Letters. 2009 Jan; 30(1):27–38.
- Chen L, Chen G, Wang F. Recommender systems based on user reviews: The state of the art. User Modeling and User-Adapted Interaction. 2015 Jun; 25(2):99–154.
- Wang H, Li WJ. Relational collaborative topic regression for recommender systems. IEEE Transactions on Knowledge and Data Engineering. 2015 May; 27(5):1343–55.
- Klašnja-Milićević A, Ivanović M, Nanopoulos A. Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artificial Intelligence Review. 2015 Dec; 44(4):571–604.
- Perugini S, Gonçalves MA, Fox EA. Recommender systems research: A connection-centric survey. Journal of Intelligent Information Systems. 2004 Sep; 23(2):107–43.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.