A scalable model for real‐time venue recommendations using MapReduce

The popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to mark the check‐ins when a venue is visited. The traditional venue recommendation systems have opted collaborative filtering to propose recommendations. However, collaborative filtering overlooked certain critical issues, including real‐time recommendations, cold start, and scalability, for venue recommendations. Moreover, real‐time physical factors such as distance from the venue are also not considered in traditional venue recommendation systems. Furthermore, parsing and processing of huge volume of unstructured data is the main challenge for conventional recommender systems, particularly when dealing with real‐time recommendations. For efficient scaling, significant computational and storage resources for recommendation systems are desired. This article proposes a Real‐Time Venue Recommendation (RTVR) model that utilizes cloud‐based MapReduce framework to process, compare, mine, and manage large data sets for generating recommendations. The results showed that the proposed model has improved accuracy for real‐time recommendations. The proposed RTVR is more scalable as it exploits a cloud‐based architecture. Moreover, the proposed techniques are verified using formal verification methods.

[1]  Christian S. Jensen,et al.  Mining significant semantic locations from GPS data , 2010, Proc. VLDB Endow..

[2]  Eunseok Lee,et al.  Collective Intelligence Based Place Recommendation System , 2012, ICAIT.

[3]  Martha Larson,et al.  Collaborative Filtering beyond the User-Item Matrix , 2014, ACM Comput. Surv..

[4]  Sajjad Ahmad Madani,et al.  Diet-Right: A Smart Food Recommendation System , 2017, KSII Trans. Internet Inf. Syst..

[5]  Hong Shen,et al.  Addressing cold-start: Scalable recommendation with tags and keywords , 2015, Knowl. Based Syst..

[6]  Mahmoud Al-Ayyoub,et al.  Toward a new approach for sorting extremely large data files in the big data era , 2018, Cluster Computing.

[7]  Hafed Zarzour,et al.  A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques , 2018, 2018 9th International Conference on Information and Communication Systems (ICICS).

[8]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[9]  Hafed Zarzour,et al.  RecDNNing: a recommender system using deep neural network with user and item embeddings , 2019, 2019 10th International Conference on Information and Communication Systems (ICICS).

[10]  Yaser Jararweh,et al.  Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp , 2019, 2019 Sixth International Conference on Software Defined Systems (SDS).

[11]  Mi-Yen Yeh,et al.  Discovering personalized routes from trajectories , 2011, LBSN '11.

[12]  Craig MacDonald,et al.  A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation , 2017, CIKM.

[13]  Mohammad Hossein Anisi,et al.  Data Collection in Smart Communities Using Sensor Cloud: Recent Advances, Taxonomy, and Future Research Directions , 2018, IEEE Communications Magazine.

[14]  Kai Chen,et al.  Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..

[15]  Trevor Cohn,et al.  Mining user behaviours: a study of check-in patterns in location based social networks , 2013, WebSci.

[16]  Mohsen Guizani,et al.  Towards Energy Saving in Computational Clouds: Taxonomy, Review, and Open Challenges , 2018, IEEE Access.

[17]  J. Bobadilla,et al.  Recommender systems survey , 2013, Knowl. Based Syst..

[18]  Wang-Chien Lee,et al.  Mining user similarity from semantic trajectories , 2010, LBSN '10.

[19]  Chi-Yin Chow,et al.  Towards location-based social networking services , 2010, LBSN '10.

[20]  Alejandro Bellogín,et al.  Improving memory-based collaborative filtering by neighbour selection based on user preference overlap , 2013, OAIR.

[21]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

[22]  Mohamed Medhat Gaber,et al.  Internet of Things and data mining: From applications to techniques and systems , 2018, WIREs Data Mining Knowl. Discov..

[23]  Cihan Kaleli.  An entropy-based neighbor selection approach for collaborative filtering , 2014, Knowl. Based Syst..

[24]  ShenHong,et al.  Addressing cold-start , 2015 .

[25]  Sudipto Guha,et al.  Clustering Data Streams , 2000, FOCS.

[26]  Justine Rochas,et al.  Solutions for Processing K Nearest Neighbor Joins for Massive Data on MapReduce , 2015, 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[27]  Yogesh Kumar Dwivedi,et al.  Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda , 2019, Int. J. Inf. Manag..

[28]  Kedar B. Sawant,et al.  Efficient Determination of Clusters in K-Mean Algorithm Using Neighborhood Distance , 2015 .

[29]  Panagiotis Symeonidis,et al.  Location-Based Social Networks , 2014 .

[30]  MengChu Zhou,et al.  Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems , 2018, ACM Comput. Surv..

[31]  R. Logesh,et al.  Exploring Hybrid Recommender Systems for Personalized Travel Applications , 2018, Cognitive Informatics and Soft Computing.

[32]  Sajjad Ahmad Madani,et al.  A comparative study of location-based recommendation systems , 2017, The Knowledge Engineering Review.

[33]  Yu Zheng,et al.  Constructing popular routes from uncertain trajectories , 2012, KDD.

[34]  Mohamed F. Mokbel,et al.  Location-based and preference-aware recommendation using sparse geo-social networking data , 2012, SIGSPATIAL/GIS.

[35]  Panagiotis Symeonidis,et al.  Recommender Systems for Location-based Social Networks , 2014, Springer Briefs in Electrical and Computer Engineering.

[36]  Abolfazl Gandomi,et al.  Comparative Study of Classification Algorithms Based On MapReduce Model , 2014 .

[37]  Prem Prakash Jayaraman,et al.  The Role of Big Data Analytics in Industrial Internet of Things , 2019, Future Gener. Comput. Syst..

[38]  Richang Hong,et al.  Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation , 2017, ACM Trans. Intell. Syst. Technol..

[39]  Moi Hoon Yap,et al.  A novel hybrid based recommendation system based on clustering and association mining , 2016, 2016 10th International Conference on Sensing Technology (ICST).

[40]  Hsiu-Sen Chiang,et al.  User-adapted travel planning system for personalized schedule recommendation , 2015, Inf. Fusion.

[41]  Srikanta Patnaik,et al.  Recent Developments in Intelligent Computing, Communication and Devices , 2017 .

[42]  Cecilia Mascolo,et al.  A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[43]  Ejaz Ahmed,et al.  Big Data Analytics in Industrial IoT Using a Concentric Computing Model , 2018, IEEE Communications Magazine.

[44]  Yerach Doytsher,et al.  Storing routes in socio-spatial networks and supporting social-based route recommendation , 2011, LBSN '11.

[45]  Gediminas Adomavicius,et al.  Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques , 2012, IEEE Transactions on Knowledge and Data Engineering.

[46]  Li Zhang,et al.  Learning similarity with cosine similarity ensemble , 2015, Inf. Sci..

[47]  Lesly Alejandra Gonzalez Camacho,et al.  Social network data to alleviate cold-start in recommender system: A systematic review , 2018, Inf. Process. Manag..

[48]  Lili He,et al.  Research and Optimization of Data Sparsity in Collaborative Filtering Algorithms , 2019 .

[49]  Albert Y. Zomaya,et al.  OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks , 2014, IEEE Transactions on Services Computing.