This paper presents a systematic review of research into package recommender systems (PRS). PRS recommend a combination of items rather than individual items, for example, multiple attractions to visit on a day trip to a city, trousers and a shirt to wear together, or multiple dishes to make up a meal. The review provides a framework for considering existing PRS research, and highlights the techniques used for package recommendation and the evaluation methods and metrics employed. It also raises many issues that warrant future research.
ResnickPIacovouNSuchakMBergstromPRiedlJ. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM; 1994. pp. 175-186.
2.
KrulwichBBurkeyC. Learning user information interests through extraction of semantically significant phrases. In: Proceedings of the AAAI spring symposium on machine learning in information access. Menlo Park: AAAI Press 1996. pp. 100-112.
3.
BalabanovicMShohamY. Fab: content-based, collaborative recommendation. Communications of the ACM.1997; 40(3): 66-73.
4.
KumarGJerbiHO’MahonyMP. Towards the Recommendation of Personalised Activity Sequences in the Tourism Domain. In: RecTour 2017 2nd Workshop on Recommenders in Tourism. Como, Italy, 27 August 2017. ACM; 2017.
5.
Yueh-MinHTien-ChiHWangKTHwangWY. A Markov-based recommendation model for exploring the transfer of learning on the web. Journal of Educational Technology & Society.2009; 12(2): 144.
6.
RicciFRokachLShapiraB. Introduction to recommender systems handbook. In: Recommender systems handbook. Springer; 2011. pp. 1-35.
7.
BaralRIyengarSLiTBalakrishnanN. CLoSe: C ontextualized Lo cation Se quence Recommender. In: Proceedings of the 12th ACM conference on recommender systems. ACM; 2018. pp. 470-474.
8.
DuganiSVDixitSBelurM. Automated adaptive sequential recommendation of travel route. In: Computing Methodologies and Communication (ICCMC), 2017 International Conference on. IEEE; 2017. pp. 284-288.
9.
KitchenhamBChartersS. Guidelines for performing systematic literature reviews in software engineering. 2007.
10.
ZhuJJShenLlWuHGTingYGangQ. A Package Recommendation Model Based on Credit and Time. DEStech Transactions on Computer Science and Engineering.2017; (wcne).
11.
WibowoATSiddharthanALinCMasthoffJ. Matrix Factorization for Package Recommendations. In: Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). CEUR-WS; 2017.
12.
WibowoATSiddharthanAMasthoffJLinC. Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. ACM; 2018. pp. 111-119.
13.
LiuGFuYChenGXiongHChenC. Modeling buying motives for personalized product bundle recommendation. ACM Transactions on Knowledge Discovery from Data (TKDD).2017; 11(3): 28.
14.
Basu RoySAmer-YahiaSChawlaADasGYuC. Constructing and exploring composite items. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM; 2010. pp. 843-854.
15.
SessomsMAnyanwuK. SkyPackage: From finding items to finding a skyline of packages on the semantic web. In: Joint International Semantic Technology Conference. Springer; 2012. pp. 49-64.
16.
ZhuTHarringtonPLiJTangL. Bundle recommendation in ecommerce. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM; 2014. pp. 657-666.
17.
BeladevMRokachLShapiraB. Recommender systems for product bundling. Knowledge-Based Systems.2016; 111: 193-206.
18.
IqbalMKovacAAryafarK. A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce. arXiv preprint arXiv180611226. 2018.
19.
ParameswaranAVenetisPGarcia-MolinaH. Recommendation systems with complex constraints: A course recommendation perspective. ACM Transactions on Information Systems (TOIS).2011; 29(4): 20.
20.
LiZPapaemmanouilOKoutrikaG. CourseNavigator: interactive learning path exploration. In: Proceedings of the Third International Workshop on Exploratory Search in Databases and the Web. ACM;2016. pp. 6-11.
21.
LiNSuriNGaoZXiaTBörnerKLiuX. Enter a job, get course recommendations. iConference2017; Proceedings Vol 2. 2017.
22.
XuJXingTVan Der SchaarM. Personalized course sequence recommendations. IEEE Transactions on Signal Processing.2016; 64(20): 5340-5352.
23.
MorsyK. Learning Course Sequencing for Course Sequence Recommendation. 2018.
24.
PanYZhangYZhangR. Combo-Recommendation Based on Potential Relevance of Items. In: Asia-Pacific Web Conference. Springer; 2016. pp. 505-517.
25.
PathakAGuptaKMcAuleyJ. Generating and personalizing bundle recommendations on Steam. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM; 2017. pp. 1073-1076.
26.
ShiraiYTsurumaKSakuraiYOyamaSMinatoSi. Incremental set recommendation based on class differences. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer;2012. pp. 183-194.
27.
MengashHBrodskyA. GCAR: A Group Composite Alternatives Recommender Based on Multi-Criteria Optimization and Voting. In: 2014 47th Hawaii International Conference on System Sciences. IEEE; 2014. pp. 1113-1121.
28.
Khabbaz XieL. Efficient Algorithms for Recommending Top-k Items and Packages. 2011.
29.
KourisPVarlamisIAlexandridisG. A package recommendation framework based on collaborative filtering and preference score maximization. In: International Conference on Engineering Applications of Neural Networks. Springer; 2017. pp. 477-489.
30.
XieMLakshmananLVWoodPT. Composite recommendations: from items to packages. Frontiers of Computer Science.2012; 6(3): 264-277.
31.
InterdonatoRRomeoSTagarelliAKarypisG. IEEE A versatile graph-based approach to package recommendation. 2013; pp. 857-864.
32.
QiSMamoulisNPitouraETsaparasP. Recommending packages with validity constraints to groups of users. Knowledge and Information Systems.2018; 54(2): 345-374.
33.
DoLLauwHWWangK. Mining revenue-maximizing bundling configuration. Proceedings of the VLDB Endowment.2015; 8(5): 593-604.
34.
SharmaMHarperFMKarypisG. Learning from Sets of Items in Recommender Systems. arXiv preprint arXiv1904 12643. 2019.
35.
LeroyVAmer-YahiaSGaussierEMirisaeeH. Building representative composite items. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM; 2015. pp. 1421-1430.
36.
OrtizRHChasiRBChalcoCI. Clustering-Based Recommender System: Bundle Recommendation Using Matrix Factorization to Single User and User Communities. In: International Conference on Applied Human Factors and Ergonomics. Springer; 2018. pp. 330-338.
37.
KourisPVarlamisIAlexandridisGStafylopatisA. A versatile package recommendation framework aiming at preference score maximization. Evolving Systems.2018; pp. 1-19.
38.
YuWLiLXuXWangDWangJChenS. ProductRec: Product Bundle Recommendation Based on User’s Sequential Patterns in Social Networking Service Environment. In: 2017 IEEE International Conference on Web Services (ICWS). IEEE; 2017. pp. 301-308.
39.
MengashHBrodskyA. Tailoring Group Package Recommendations to Large Heterogeneous Groups Based on Multi-Criteria Optimization. In: 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE; 2016. pp. 1537-1546.
40.
Amer-YahiaSBonchiFCastilloCFeuersteinEMendez-DiazIZabalaP. Composite retrieval of diverse and complementary bundles. IEEE Transactions on Knowledge and Data Engineering.2014; 26(11): 2662-2675.
41.
BotaHZhouKJoseJMLalmasM. Composite retrieval of heterogeneous web search. In: Proceedings of the 23rd international conference on World wide web. ACM; 2014. pp. 119-130.
42.
VillavicencioCSchiaffinoSDíaz PaceJA. Solving Package Recommendation Problems with Item Relations and Variable Size. In: Argentine Symposium on Artificial Intelligence (ASAI 2015)-JAIIO; 44 (Rosario, 2015); 2015.
43.
XieMLakshmananLVWoodPT. Generating top-k packages via preference elicitation. Proceedings of the VLDB Endowment.2014; 7(14): 1941-1952.
44.
FangYXiaoXWangXLanH. Customized Bundle Recommendation by Association Rules of Product Categories for Online Supermarkets. In: 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). IEEE; 2018. pp. 472-475.
45.
Amer-YahiaSGaussierELeroyVPilourdaultJBorromeoRMToyamaM. Task composition in crowdsourcing. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE; 2016. pp. 194-203.
46.
DornCSkopikFSchallDDustdarS. Interaction mining and skill-dependent recommendations for multi-objective team composition. Data & Knowledge Engineering. 2011; 70(10): 866-891.
47.
CastilloLArmengolEOnaindíaESebastiáLGonzález-BoticarioJRodríguezA, et al. samap: An user-oriented adaptive system for planning tourist visits. Expert Systems with Applications.2008; 34(2): 1318-1332.
48.
BenouaretILenneD. Recommending diverse and personalized travel packages. In: International Conference on Database and Expert Systems Applications. Springer;2017. pp. 325-339.
49.
ChenGWuSZhouJTungAK. Automatic itinerary planning for traveling services. IEEE Transactions on Knowledge and Data Engineering.2013; 26(3): 514-527.
50.
GionisALappasTPelechrinisKTerziE. Customized tour recommendations in urban areas. In: Proceedings of the 7th ACM international conference on Web search and data mining. ACM; 2014. pp. 313-322.
51.
HtiRDesarkarMS. Personalized Tourist Package Recommendation Using Graph Based Approach. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. ACM; 2018. pp. 257-262.
52.
ReddyCASubramaniyaswamyV. An enhanced travel package recommendation system based on location dependent social data. Indian Journal of Science and Technology.2015; 8(16): 1.
53.
TanCLiuQChenEXiongHWuX. Object-oriented travel package recommendation. ACM Transactions on Intelligent Systems and Technology (TIST).2014; 5(3): 43.
54.
FuCYHuMCLaiJHWangHWuJL. Travelbuddy: interactive travel route recommendation with a visual scene interface. In: International Conference on Multimedia Modeling. Springer;2014. pp. 219-230.
55.
ZhaoSZhaoTYangHLyuMRKingI. STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In: Thirtieth AAAI conference on artificial intelligence;2016.
56.
ZhangCLiangHWangKSunJ. Personalized trip recommendation with poi availability and uncertain traveling time. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM; 2015. pp. 911-920.
57.
HsiehHPLiCT. Constructing trip routes with user preference from location check-in data. In: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication. ACM; 2013. pp. 195-198.
58.
HerzogDMassoudHWörndlW. Routeme: A mobile recommender system for personalized, multi-modal route planning. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. ACM; 2017. pp. 67-75.
59.
WörndlWHefeleAHerzogD. Recommending a sequence of interesting places for tourist trips. Information Technology & Tourism.2017; 17(1): 31-54.
60.
LaßHerzogW. Context-Aware Tourist Trip Recommendations. In: RecTour 2017 2nd Workshop on Recommenders in Tourism. RecTour; 2017. pp. 18-25.
61.
HerzogDWörndlW. A Travel Recommender System for Combining Multiple Travel Regions to a Composite Trip. CBRecSys@ RecSys.2014; 1245: 42-48.
62.
ZhangCLiangHWangK. Trip recommendation meets real-world constraints: POI availability, diversity, and traveling time uncertainty. ACM Transactions on Information Systems (TOIS).2016; 35(1): 5.
63.
LiangHWangK. Top-k route search through submodularity modeling of recurrent POI features. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM; 2018. pp. 545-554.
64.
WörndlWLudwigMHerzogD. Recommending Customized Trips Based on the Combination of Travel Regions. In: ENTER 2015 Conference, Lugano, Switzerland, February 3–6 2015.
65.
InterdonatoRTagarelliA. Personalized recommendation of points-of-interest based on multilayer local community detection. In: International Conference on Social Informatics. Springer; 2017. pp. 552-571.
66.
SangJMeiTSunJTXuCLiS. Probabilistic sequential POIs recommendation via check-in data. In: Proceedings of the 20th international conference on advances in geographic information systems. ACM; 2012. pp. 402-405.
67.
WeiLYZhengYPengWC. Mining popular routes from social media. In: Multimedia Data Mining and Analytics. Springer; 2015. pp. 93-116.
68.
LuEHCFangSHTsengVS. Integrating tourist packages and tourist attractions for personalized trip planning based on travel constraints. GeoInformatica.2016; 20(4): 741-763.
69.
HuGShaoJShenFHuangZShenHT. Unifying multi-source social media data for personalized travel route planning. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM; 2017. pp. 893-896.
70.
HeJQiJRamamohanaraoK. A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations. arXiv preprint arXiv180808023. 2018.
71.
YangLZhangRSunHGuoXHuaiJ. A Tourist Itinerary Planning Approach Based on Ant Colony Algorithm. In: International Conference on Web-Age Information Management. Springer; 2012. pp. 399-404.
72.
HsiehHPLiCTLinSD. Measuring and recommending time-sensitive routes from location-based data. ACM Transactions on Intelligent Systems and Technology (TIST).2014; 5(3): 45.
73.
JiangSQianXMeiTFuY. Personalized travel sequence recommendation on multi-source big social media. IEEE Transactions on Big Data.2016; 2(1): 43-56.
74.
RakeshVJadhavNKotovAReddyCK. Probabilistic social sequential model for tour recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM; 2017. pp. 631-640.
75.
LiJYangYLiuW. Exploring personalized travel route using POIs. International Journal of Computer Theory and Engineering.2015; 7(2): 126.
76.
LiuQChenEXiongHGeYLiZWuX. A cocktail approach for travel package recommendation. IEEE Transactions on Knowledge and Data Engineering.2012; 26(2): 278-293.
YuZXuHYangZGuoB. Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems.2015; 46(1): 151-158.
79.
BaralRIyengarSLiTZhuX. HiCaPS: hierarchical contextual POI sequence recommender. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM; 2018. pp. 436-439.
80.
Biwas CreedW. Itinerary Planning Using Top-k Package Recommendation and Multiple Constraints.
81.
ChenXZhangYMaPLiCXingC. A package generation and recommendation framework based on travelogues. In: 2015 IEEE 39th Annual Computer Software and Applications Conference. vol. 2. IEEE; 2015. pp. 692-701.
82.
BoulakbechMChenikiNMessaiNSamYDevogeleT. Linked Data Graphs for Semantic Data Integration in the CART System. In: International Conference on Web Engineering. Springer; 2018. pp. 221-226.
83.
XiongHLiuZ. A situation information integrated personalized travel package recommendation approach based on TD-LDA model. In: 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC). IEEE; 2015. pp. 32-37.
84.
ChangHTChangYMTsaiMT. ATIPS: automatic travel itinerary planning system for domestic areas. Computational intelligence and neuroscience.2016; 2016: 1.
85.
MikhailovSKashevnikA. An Ontology for Service Semantic Interoperability in the Smartphone-Based Tourist Trip Planning System. In: 2018 23rd Conference of Open Innovations Association (FRUCT). IEEE; 2018. pp. 240-245.
86.
AlsayasnehMAmer-YahiaSGaussierELeroyVPilourdaultJBorromeoRM, et al. Personalized and diverse task composition in crowdsourcing. IEEE Transactions on Knowledge and Data Engineering. 2017; 30(1): 128-141.
87.
ChenCZhangDGuoBMaXPanGWuZ. TripPlanner: Personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Transactions on Intelligent Transportation Systems.2014; 16(3): 1259-1273.
88.
JeffriesWBrodskyA. Composite Alternative Pareto Optimal Recommendation System with Individual Utility Extraction (CAPORS-IUX). In: ICEIS (1); 2018. pp. 328-335.
89.
TintarevNMasthoffJ. Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction. 2012 Oct; 22(4): 399-439. Available from: 10.1007/s11257-011-9117-5.
90.
ShapiroC. Optimal pricing of experience goods. The Bell Journal of Economics.1983; pp. 497-507.
91.
ChoYImIFjermestadJRoxanne HiltzS. The impact of product category on customer dissatisfaction in cyberspace. Business Process Management Journal.2003; 9(5): 635-651.
92.
LabandDN. An objective measure of search versus experience goods. Economic Inquiry.1991; 29(3): 497-509.
93.
MurphyPEEnisBM. Classifying products strategically. Journal of Marketing.1986; 50(3): 24-42.
94.
TintarevNMasthoffJ. Over-and underestimation in different product domains. Workshop on Recommender Systems associated with ECAI;2008. pp. 14-19.
95.
BaccigalupoCPlazaE. Case-based sequential ordering of songs for playlist recommendation. In: European Conference on Case-Based Reasoning. Springer; 2006. pp. 286-300.
96.
LiuHHuJRauterbergM. Music playlist recommendation based on user heartbeat and music preference. In: 2009 International Conference on Computer Technology and Development. vol. 1. IEEE; 2009. pp. 545-549.
97.
LiebmanESaar-TsechanskyMStoneP. Dj-mc A reinforcement-learning agent for music playlist recommendation. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems; 2015. pp. 591-599.
98.
BobadillaJOrtegaFHernandoAGutiérrezA. Recommender systems survey. Knowledge-Based Systems.2013; 46: 109-132.
99.
CremonesiPKorenYTurrinR. Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the fourth ACM conference on Recommender systems. ACM; 2010. pp. 39-46.
100.
ShaniGGunawardanaA. Evaluating recommendation systems. In: Recommender systems handbook. Springer; 2011. pp. 257-297.
101.
WibowoAT. Generating Pseudotransactions for Improving Sparse Matrix Factorization. In: Proceedings of the 10th ACM Conference on Recommender Systems. RecSys ’16. New York, NY, USA: ACM; 2016. pp. 439-442. Available from: 10.1145/2959100.2959107.
