Recommender Systems in the Offline Retailing Domain: A Systematic Literature Review

Recommender systems have over the years proved to be effective in overcoming the challenges related to the incredible growth of the information on the Web, and nowadays they are evidently popular in a variety of apparently disparate domains, e.g., e-commerce, tourism and healthcare. Nonetheless, no Systematic Literature Review (SLR), and even no literature survey, aimed at analyzing the current state of academic and industrial research knowledge on recommender systems for the offline retailing domain is reported in the literature. There is, therefore, a need for conducting a SLR that allows revealing the trends and challenges in the research and development of recommender systems for that domain. In this chapter, we present the details about the planning, execution and analysis of results of a SLR of the state of the art of recommender systems for the offline retailing domain. The findings of the analysis of results shed light, among other things, on the necessity of further research on recommendation systems and algorithms for the offline retailing domain, specially, for small local stores and chain stores in the categories of department stores, drugstores and convenience stores.

[1]  Maurizio Morisio,et al.  Hybrid recommender systems: A systematic literature review , 2019, Intell. Data Anal..

[2]  Athanasios K. Tsakalidis,et al.  Customer Behaviour Analysis for Recommendation of Supermarket Ware , 2016, AIAI.

[3]  Jinghua Huang,et al.  A Survey of E-Commerce Recommender Systems , 2007, 2007 International Conference on Service Systems and Service Management.

[4]  Mimmo Parente,et al.  An AmI-based and privacy-preserving shopping mall model , 2017, Human-centric Computing and Information Sciences.

[5]  Jyot Talreja,et al.  Intelligent Recommender System Using Shopper's Path and Purchase Analysis , 2010, 2010 International Conference on Computational Intelligence and Communication Networks.

[6]  Michail Vlachos,et al.  Unsupervised Sparse Matrix Co-clustering for Marketing and Sales Intelligence , 2012, PAKDD.

[7]  Gamal Hussein,et al.  Mobile recommender system analysis & design , 2009, 2009 First International Conference on Networked Digital Technologies.

[8]  M. Alex Syaekhoni,et al.  Analyzing customer behavior from shopping path data using operation edit distance , 2016, Applied Intelligence.

[9]  Alexander Ilic,et al.  Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems , 2016, RecSys.

[10]  Altan Koçyigit,et al.  An Adjusted Recommendation List Size Approach for Users' Multiple Item Preferences , 2016, AIMSA.

[11]  Takashi Sonoda,et al.  Discount Sensitive Recommender System for Retail Business , 2015, EMPIRE@RecSys.

[12]  Kutuzova Tatiana,et al.  Market basket analysis of heterogeneous data sources for recommendation system improvement , 2018 .

[13]  Georg Waltner,et al.  MANGO - Mobile Augmented Reality with Functional Eating Guidance and Food Awareness , 2015, ICIAP Workshops.

[14]  Luca Becchetti,et al.  Recommending items in pervasive scenarios: models and experimental analysis , 2011, Knowledge and Information Systems.

[15]  Kihwan Nam,et al.  Group recommender system for store product placement , 2018, Data Mining and Knowledge Discovery.

[16]  Altan Koçyigit,et al.  mRHR: A Modified Reciprocal Hit Rank Metric for Ranking Evaluation of Multiple Preferences in Top-N Recommender Systems , 2016, AIMSA.

[17]  Madhusudana V. S. Shashanka,et al.  Mining Retail Transaction Data for Targeting Customers with Headroom - A Case Study , 2009, AIAI.

[18]  Yeong-Chyi Lee,et al.  fuzzy coherent rule mining algorithm , 2013 .

[19]  Jinjun Chen,et al.  Integrating Collaborative Filtering and Association Rule Mining for Market Basket Recommendation , 2018, WISE.

[20]  Toshikazu Kato,et al.  Analysis of Customer Preference through Unforced Natural Passive Observation , 2013, HCI.

[21]  Pasquale Lops,et al.  A Multimodal Framework for Recognizing Emotional Feedback in Conversational Recommender Systems , 2015, EMPIRE@RecSys.

[22]  Takao Terano,et al.  Extracting the Potential Sales Items from the Trend Leaders with the ID-POS Data , 2009, KES.

[23]  George Angelos Papadopoulos,et al.  The UbiCARS Model-Driven Framework: Automating Development of Recommender Systems for Commerce , 2018, AmI.

[24]  Alex Thomo,et al.  From Recommendation to Profile Inference (Rec2PI): A Value-added Service to Wi-Fi Data Mining , 2016, CIKM.

[25]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[26]  Georg Waltner,et al.  Personalized Dietary Self-Management Using Mobile Vision-Based Assistance , 2017, ICIAP Workshops.

[27]  Jaap Ham,et al.  Does Trigger Location Matter? The Influence of Localization and Motivation on the Persuasiveness of Mobile Purchase Recommendations , 2015, PERSUASIVE.

[28]  Pierre-Majorique Léger,et al.  In AI We Trust: Characteristics Influencing Assortment Planners' Perceptions of AI Based Recommendation Agents , 2018, HCI.

[29]  Yansen Su,et al.  A multi-objective evolutionary approach for mining frequent and high utility itemsets , 2018, Appl. Soft Comput..

[30]  Alexandros Nanopoulos,et al.  Recommender systems based on quantitative implicit customer feedback , 2014, Decis. Support Syst..

[31]  Dimosthenis Kyriazis,et al.  Collaborative Filtering for Producing Recommendations in the Retail Sector , 2017, EMCIS.

[32]  John Riedl,et al.  E-Commerce Recommendation Applications , 2004, Data Mining and Knowledge Discovery.

[33]  Cheng-Hsiung Weng.  Revenue prediction by mining frequent itemsets with customer analysis , 2017, Eng. Appl. Artif. Intell..

[34]  Shih-Jung Wu,et al.  Direct mail promotion mechanisms and their application in supermarkets , 2018, The Journal of Supercomputing.

[35]  Ayhan Demiriz,et al.  Analyzing Price Data to Determine Positive and Negative Product Associations , 2009, ICONIP.

[36]  Anna Monreale,et al.  Discovering temporal regularities in retail customers’ shopping behavior , 2018, EPJ Data Science.

[37]  Norha M. Villegas,et al.  Exploiting Context Information to Improve the Precision of Recommendation Systems in Retailing , 2017 .

[38]  Quan Chen,et al.  Domain Knowledge Based Personalized Recommendation Model and Its Application in Cross-selling , 2012, ICCS.

[39]  Yan Gao,et al.  Dynamic-K Recommendation with Personalized Decision Boundary , 2017, CCIR.

[40]  Jae Kwon Bae,et al.  Integration of heterogeneous models to predict consumer behavior , 2010, Expert Syst. Appl..

[41]  Arup Roy,et al.  A bio-inspired perspective towards retail recommender system: Investigating optimization in retail inventory , 2012, 2012 12th International Conference on Intelligent Systems Design and Applications (ISDA).

[42]  Juliana Sutanto,et al.  Achieving Targeted Mobile Advertisements While Respecting Privacy , 2012, MobiCASE.

[43]  Vedran Podobnik,et al.  RecoMMobile: A spatiotemporal recommender system for mobile users , 2011, SoftCOM 2011, 19th International Conference on Software, Telecommunications and Computer Networks.

[44]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[45]  Mykola Pechenizkiy,et al.  Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales , 2012, SGAI Conf..

[46]  Xu Chi,et al.  Profit estimation error analysis in recommender systems based on association rules , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[47]  Raúl Parada,et al.  Measuring user-object interactions in IoT spaces , 2015, 2015 IEEE International Conference on RFID Technology and Applications (RFID-TA).

[48]  Julián Villegas,et al.  Assisting system for grocery shopping navigation and product recommendation , 2017, 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE).

[49]  Evangelia Christakopoulou,et al.  HOSLIM: Higher-Order Sparse LInear Method for Top-N Recommender Systems , 2014, PAKDD.

[50]  Özer Uygun,et al.  Selecting Display Products for Furniture Stores Using Fuzzy Multi-criteria Decision Making Techniques , 2018, EANN.

[51]  Licia Capra,et al.  Continuous hyperparameter optimization for large-scale recommender systems , 2013, 2013 IEEE International Conference on Big Data.

[52]  Katerina Pramatari,et al.  Retail business analytics: Customer visit segmentation using market basket data , 2018, Expert Syst. Appl..

[53]  Kei Miyazaki,et al.  A Framework of ASP for Shopping Path Analysis , 2017, 2017 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE).

[54]  Wang Dong,et al.  Recommendation system technologies of intelligent large-scale shopping mall , 2012, Proceedings of 2012 2nd International Conference on Computer Science and Network Technology.

[55]  François Fouss,et al.  Recommender systems: the case of repeated interaction in matrix factorization , 2017, WI.

[56]  Ratna Babu Chinnam,et al.  Deriving Business Recommendations for Franchises Using Competitive Learning Driven MLP-Based Clustering , 2017, EANN.

[57]  Raúl Parada,et al.  Enhancing the shopping experience through RFID in an actual retail store , 2013, UbiComp.

[58]  Shao-Lun Lee.  Commodity recommendations of retail business based on decisiontree induction , 2010, Expert Syst. Appl..

[59]  Yi-Chong Zeng,et al.  Enhancing retailer marketing with an facial recognition integrated recommender system , 2015, 2015 IEEE International Conference on Consumer Electronics - Taiwan.

[60]  Jarogniew Rykowski,et al.  In-Store Proximity Marketing by Means of IoT Devices , 2018, PRO-VE.

[61]  Mengru Tu,et al.  A Context-Aware Recommender System Framework for IoT based Interactive Digital Signage in Urban Space , 2016, Urb-IoT.

[62]  Lior Rokach,et al.  Recommender Systems for Product Bundling , 2016, RecSys Posters.

[63]  Ashok Kumar Das,et al.  An efficient fast algorithm for discovering closed+ high utility itemsets , 2016, Applied Intelligence.

[64]  João M. F. Rodrigues,et al.  An Object Visit Recommender Supported in Multiple Visitors and Museums , 2017, HCI.

[65]  Thorben Keller,et al.  Cosibon: an E-commerce like platform enabling bricks-and-mortar stores to use sophisticated product recommender systems , 2014, RecSys '14.

[66]  Rong Gao,et al.  Big data based retail recommender system of non E-commerce , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[67]  Rafael Valencia-García,et al.  RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes , 2015, Expert Syst. Appl..

[68]  Jiang-Liang Hou,et al.  An RFID-based Shopping Service System for retailers , 2011, Adv. Eng. Informatics.

[69]  Chang-Tien Lu,et al.  Shopaholic: a crowd-sourced spatio-temporal product-deals evaluation system (demo paper) , 2014, IWGS.

[70]  Quang Nhat Nguyen,et al.  User Preferences Elicitation and Exploitation in a Push-Delivery Mobile Recommender System , 2012, ICCASA.

[71]  Giang Nguyen,et al.  Decision influence and proactive sale support in a chain of convenience stores , 2017, 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES).

[72]  Tsvi Kuflik,et al.  When user modeling intersects software engineering: the info-bead user modeling approach , 2015, User Modeling and User-Adapted Interaction.

[73]  Hiroyuki Kidokoro,et al.  Effectiveness of Cooperative Customer Navigation from Robots around a Retail Shop , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[74]  Quan Chen,et al.  Domain-Knowledge Driven Recommendation Method and Its Application , 2011, 2011 Fourth International Joint Conference on Computational Sciences and Optimization.

[75]  Jong Hyuk Park,et al.  Real-time smartphone sensing and recommendations towards context-awareness shopping , 2013, Multimedia Systems.

[76]  Dong Aihua,et al.  Hybrid Product Recommender System for Apparel Retailing Customers , 2010, 2010 WASE International Conference on Information Engineering.

[77]  Yvonne Rogers,et al.  Decision-making in the aisles: informing, overwhelming or nudging supermarket shoppers? , 2012, Personal and Ubiquitous Computing.