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A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion

Mohammad Aliannejadi, Dimitrios Rafailidis, Fabio Crestani
2018 Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval - ICTIR '18  
Recommending venues plays a critical rule in satisfying users' needs on location-based social networks.  ...  We further introduce three example similarity measures based on venues' contents and locations.  ...  In our approach we compute a contextual similarity between two venues l i and l j based on their content and location.  ... 
doi:10.1145/3234944.3234945 dblp:conf/ictir/AliannejadiRC18 fatcat:wtidgy2r2fging5z3ott7gsyku

Cross-Social Network Collaborative Recommendation

Aleksandr Farseev, Denis Kotkov, Alexander Semenov, Jari Veijalainen, Tat-Seng Chua
2015 Proceedings of the ACM Web Science Conference on ZZZ - WebSci '15  
We hypothesize that the integration of data from multiple social networks could boost the performance of recommender systems.  ...  In our study, we perform cross-social network collaborative recommendation and show that fusing multi-source data enables us to achieve higher recommendation performance as compared to various single-source  ...  In particular, venue category recommendation (e.g. restaurant, museum, or park) is an important task in tourism and advertisement for suggesting interesting venues near users' current location.  ... 
doi:10.1145/2786451.2786504 dblp:conf/websci/FarseevKSVC15 fatcat:ukq7dghqkzdnlhmwefvfnlrpwq

Venue Suggestion Using Social-Centric Scores [article]

Mohammad Aliannejadi, Fabio Crestani
2019 arXiv   pre-print
User modeling is a very important task for making relevant suggestions of venues to the users.  ...  These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations.  ...  Acknowledgment This work was partially supported by the Swiss National Science Foundation (SNSF) under the project "Relevance Criteria Combination for Mobile IR (Rel-MobIR)."  ... 
arXiv:1803.08354v2 fatcat:3hblbzzp5zgbxhzlmsjt5qcof4

A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation [article]

Mohammad Aliannejadi and Dimitrios Rafailidis and Fabio Crestani
2019 arXiv   pre-print
The popularity of location-based social networks (LBSNs) has led to a tremendous amount of user check-in data.  ...  While recent work has explored the idea of adopting collaborative ranking (CR) for recommendation, there have been few attempts to incorporate temporal information for POI recommendation using CR.  ...  Compared Methods We compare our Joint Two-Phase Collaborative Ranking (JTCR) model with approaches that consider geographical influence for POI recommendation and approaches based on collaborative ranking  ... 
arXiv:1909.07131v1 fatcat:c7ge4utjkvfull2d37s4bvirgu

Cloud-Based Remote Venue Recommendation Framework

Ms. Sonawane K. C., Ms. Ponde S. S.
2017 IJARCCE  
Moreover, the Weighted Sum Approach (WSA) is implemented for Pyramid Maintenance Algorithm (PMA) is applied for vector optimization to provide optimal suggestions to the users about a venue.  ...  Most of the existing recommendation systems based their models on collaborative filtering approaches that make them simple to implement.  ...  For instance, in the case of G-BORF, when there is no similarity between two users" preferred locations, the venue will be suggested to the current user on the bases of user-to-venue closeness.  ... 
doi:10.17148/ijarcce.2017.6119 fatcat:ite6zbglgzgjndfk4a2ejoxyqa

A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation

Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
Matrix Factorisation (MF) is a popular Collaborative Filtering (CF) technique that can suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues.  ...  In this paper, we propose a Deep Recurrent Collaborative Filtering framework (DRCF) with a pairwise ranking function that aims to capture user-venue interactions in a CF manner from sequences of observed  ...  Re- We propose a Deep Recurrent Collaborative Filtering frame work (DRCF) with a pairwise ranking function for venue recommendation.  ... 
doi:10.1145/3132847.3133036 dblp:conf/cikm/ManotumruksaMO17 fatcat:nbkqgavhbbgxteaplyc6dmqe5u

Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion

Mohammad Aliannejadi, Fabio Crestani
2017 Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17  
Personalized context-aware venue suggestion plays a critical role in satisfying the users' needs on location-based social networks (LBSNs).  ...  In this paper, we present a set of novel scores to measure the similarity between a user and a candidate venue in a new city. e scores are based on user's history of preferences in other cities as well  ...  A major challenge for venue suggestion is how to model the user pro le, that should be built based on the user feedback on previously visited places.  ... 
doi:10.1145/3077136.3080754 dblp:conf/sigir/AliannejadiC17 fatcat:y5nunqs3ubbvxccr5zrox4k75m

Diversifying contextual suggestions from location-based social networks

M-Dyaa Albakour, Romain Deveaud, Craig Macdonald, Iadh Ounis
2014 Proceedings of the 5th Information Interaction in Context Symposium on - IIiX '14  
For categorising the venues, we use the category classifications employed by location-based social networks such as FourSquare, urban guides such as Yelp, and a large collection of web pages, the ClueWeb12  ...  In this paper, we study the emerging Information Retrieval (IR) task of contextual suggestion in location-based social networks.  ...  [19] used random walk with a collaborative filtering approach based on latent space models and computed a variety of similarity criteria with venue's visit frequencies on the LBSN. Bao et al.  ... 
doi:10.1145/2637002.2637018 dblp:conf/iiix/AlbakourDMO14 fatcat:cgyz6zpmcba6xn6rjg6a2ramjm

Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data [article]

Jinfeng Yi, Qi Lei, Wesley Gifford, Ji Liu, Junchi Yan
2017 arXiv   pre-print
In order to efficiently solve the proposed framework, we propose a parameter-free and scalable optimization algorithm by effectively exploring the sparse and low-rank structure of the tensor.  ...  Identifying significant location categories visited by mobile users is the key to a variety of applications.  ...  In contrast, this paper infers the user's location categories together from a unified model that collaboratively locates each user.  ... 
arXiv:1702.06362v3 fatcat:bi6mb4qfzjctzklrgxsqtaw7j4

Predicting Contextually Appropriate Venues in Location-Based Social Networks [chapter]

Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
2016 Lecture Notes in Computer Science  
The effective suggestion of venues that are appropriate for a user to visit is a challenging problem, as the appropriateness of a venue can depend on particular contextual aspects, such as the duration  ...  This paper proposes a supervised approach that predicts appropriateness of venues to particular contextual aspects, by leveraging user-generated data in Location-Based Social Networks (LBSNs) such as Foursquare  ...  As a basis for our experiments, we use a personalised CAVR system based upon learning to rank -similar to that of Deveaud et al.  ... 
doi:10.1007/978-3-319-44564-9_8 fatcat:xbzch4lwnfdankhurf3jyneaju

New Assistance Site for Recommendation using Collaborative Filtering and Authenticate User using Face Detection

Ahmad Abulharish Jamil
2020 International Journal for Research in Applied Science and Engineering Technology  
creating suggestions.  ...  Virtual assistants, conjointly called intelligent colloquial systems like Google's Virtual Assistant and Apple's Siri, act with human-like responses to users' queries and end specific tasks.  ...  C Paper Name: A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion 1) Author: Mohammad Aliannejadi, Dimitrios Rafailidis 2) Description: Recommending venues play  ... 
doi:10.22214/ijraset.2020.6114 fatcat:wfn5fog4zva3tmn7janocirxxu

A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks

Anastasios Noulas, Salvatore Scellato, Neal Lathia, Cecilia Mascolo
2012 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing  
Our results pave the way to a new approach for place recommendation in location-based social systems.  ...  Finally, we propose a new model based on personalized random walks over a user-place graph that, by seamlessly combining social network and venue visit frequency data, obtains between 5 and 18% improvement  ...  Section IV describes a host of algorithms-ranging from content-based, social, and collaborative filtering (with neighborhood and latent space models) that have been used to build web recommender systems  ... 
doi:10.1109/socialcom-passat.2012.70 dblp:conf/socialcom/NoulasSLM12 fatcat:i6wgc2xelvfulkgoibi6rulcum

Privacy-preserving social recommendations in geosocial networks

Bisheng Liu, Urs Hengartner
2013 2013 Eleventh Annual Conference on Privacy, Security and Trust  
Geosocial networks like Foursquare have enabled people to conveniently share their whereabouts with their friends online, such as sharing check-ins at visited venues.  ...  The results suggest that the proposed privacypreserving framework is feasible on a smart phone and only slightly affects the overall performance of recommender systems. I.  ...  ACKNOWLEDGMENT We thank the anonymous reviewers for their helpful comments.  ... 
doi:10.1109/pst.2013.6596038 dblp:conf/pst/LiuH13 fatcat:qbvjbkzk5bhf7nzcxjyukhu45y

A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla.  ...  and other similar ranking approaches.  ...  [3, 7, 16, 25, 28, 29] ) apply Collaborative Filtering (CF) techniques to suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues.  ... 
doi:10.1145/3132847.3132985 dblp:conf/cikm/ManotumruksaMO17a fatcat:m5cdf4zfmzgjrdfck3saripwjq

Who, What, When, and Where

Preeti Bhargava, Thomas Phan, Jiayu Zhou, Juhan Lee
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15  
In this paper, we present a system and an approach for performing multidimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization  ...  Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times  ...  [11] experiment with several models such as collaborative filtering, preference-based, distance-based, and a weighted combination of these to provide activity recommendations via Magitti.  ... 
doi:10.1145/2736277.2741077 dblp:conf/www/BhargavaPZL15 fatcat:dnwfzjo47zhyzdvuyb4wbjz3la
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