ABSTRACT
Recommender systems are commonly used for recommending items such as products, restaurants or other points-of-interest (POI). In our automotive scenario, the driver of a car gets recommendations for gas stations. Thereby, item attributes such as price or location are important, but also context data such as the current time, location or gas level of the car when requesting the recommendation. Our approach is based on Multi-Criteria Decision Making (MCDM) methods to calculate scores on several dimensions. We used utility functions modeling the importance of different route context elements which were derived from a preliminary user study, among other information. In addition, our system performs contextual pre- and post-filtering to reduce the number of considered items. The evaluation showed that our approach produced reasonably good results in comparison with the assessment of users in a second study.
- G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems, 23(1):103--145, Jan. 2005. Google ScholarDigital Library
- G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In 2nd ACM Conference on Recommender systems, pages 335--336, New York, New York, USA, 2008. ACM Press. Google ScholarDigital Library
- G. Adomavicius and A. Tuzhilin. Context-Aware Recommender Systems. In Recommender Systems Handbook: A Complete Guide for Research Scientists and Practitioners, chapter 7, pages 217--250. Springer, 1st edition, 2010.Google Scholar
- S. Börzsönyi, D. Kossmann, and K. Stocker. The Skyline Operator. In Proceedings of the 17th International Conference on Data Engineering, pages 421--430, 2001. Google ScholarDigital Library
- R. D. Burke. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, pages 1--29, 2002. Google ScholarDigital Library
- C.-y. Chan, H. V. Jagadish, K.-l. Tan, A. K. H. Tung, and Z. Zhang. Finding k-Dominant Skylines in High Dimensional Space. pages 503--514, 2006. Google ScholarDigital Library
- J. Fülöp. Introduction to Decision Making Methods. In BDEI-3 Workshop, Washington, 2005.Google Scholar
- E. Horvitz, P. Koch, and M. Subramani. Mobile Opportunistic Planning: Methods and Models. User Modeling, 4511:228--237, 2007. Google ScholarDigital Library
- E.-y. Kang, H. Kim, and J. Cho. Personalization Method for Tourist Point of Interest (POI) Recommendation. Knowledge-Based Intelligent Information and Engineering Systems, 4251:392--400, 2006. Google ScholarDigital Library
- K. Kodama, Y. Iijima, X. Guo, and Y. Ishikawa. Skyline queries based on user locations and preferences for making location-based recommendations. In International Workshop on Location Based Social Networks, page 9, New York, New York, USA, 2009. ACM Press. Google ScholarDigital Library
- N. Manouselis and C. Costopoulou. Analysis and Classification of Multi-Criteria Recommender Systems. World Wide Web, 10(4):415--441, 2007. Google ScholarDigital Library
- S. Shekhar and J. S. Yoo. Processing in-route nearest neighbor queries. In Proceedings of the eleventh ACM international symposium on Advances in geographic information systems, pages 9--16, New York, 2003. ACM Press. Google ScholarDigital Library
Index Terms
- Context-aware POI recommendations in an automotive scenario using multi-criteria decision making methods
Recommendations
Using multi-criteria decision making for personalized point-of-interest recommendations
SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information SystemsLocation-based business review (LBBR) sites (e.g., Yelp) provide us a possibility to recommend new points of interest (POIs) for users. The geographical position and category of POIs have been considered as two major factors in modeling users' ...
User-Oriented Context Suggestion
UMAP '16: Proceedings of the 2016 Conference on User Modeling Adaptation and PersonalizationRecommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' ...
Context suggestion: empirical evaluations vs user studies
WI '17: Proceedings of the International Conference on Web IntelligenceRecommender System has been successfully applied to assist user's decision making by providing a list of recommended items. Context-aware recommender system additionally incorporates contexts (such as time and location) into the system to improve the ...
Comments