ABSTRACT
Online social networks and mash-up services create opportunities to connect different web services otherwise isolated. Specifically in the case of news, users are very much exposed to news articles while performing other activities, such as social networking or web searching. Browsing behavior aimed at the consumption of news, especially in relation to the visits coming from other domains, has been mainly overlooked in previous work. To address that, we build a BrowseGraph out of the collective browsing traces extracted from a large viewlog of Yahoo News (0.5B entries), and we define the ReferrerGraph as its subgraph induced by the sessions with the same referrer domain. The structural and temporal properties of the graph show that browsing behavior in news is highly dependent on the referrer URL of the session, in terms of type of content consumed and time of consumption. We build on this observation and propose a news recommender that addresses the cold-start problem: given a user landing on a page of the site for the first time, we aim to predict the page she will visit next. We compare 24 flavors of recommenders belonging to the families of content-based, popularity-based, and browsing-based models. We show that the browsing-based recommender that takes into account the referrer URL is the best performing, achieving a prediction accuracy of 48% in conditions of heavy data sparsity.
Supplemental Material
- D. Agarwal and B.-C. Chen. da: Matrix factorization through latent dirichlet allocation. In WSDM. ACM, 2010. Google ScholarDigital Library
- C. Castillo, M. El-Haddad, J. Pfeffer, and M. Stempeck. Characterizing the life cycle of online news stories using social media reactions. In CSCW. ACM, 2014. Google ScholarDigital Library
- L. Chiarandini, P. Grabowicz, M. Trevisiol, and A. Jaimes. Leveraging browsing patterns for topic discovery and photostream recommendation. In ICWSM. AAAI, 2013.Google Scholar
- L. Chiarandini, M. Trevisiol, and A. Jaimes. Discovering Social Photo Navigation Patterns. In ICME. ACM, 2012. Google ScholarDigital Library
- A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization. In WWW. ACM, 2007. Google ScholarDigital Library
- G. M. Del Corso, A. Gullí, and F. Romani. Ranking a stream of news. In WWW. ACM, 2005. Google ScholarDigital Library
- F. Figueiredo, F. Benevenuto, and J. Almeida. The Tube over Time: Characterizing Popularity Growth of YouTube Videos. In WSDM, 2011. Google ScholarDigital Library
- J. Hu, H.-J. Zeng, H. Li, C. Niu, and Z. Chen. Demographic prediction based on user's browsing behavior. In WWW, 2007. Google ScholarDigital Library
- R. Kumar and A. Tomkins. A characterization of online browsing behavior. In WWW. ACM, 2010. Google ScholarDigital Library
- L. Li, D. Wang, T. Li, D. Knox, and B. Padmanabhan. SCENE: a scalable two-stage personalized news recommendation system. In SIGIR. ACM, 2011. Google ScholarDigital Library
- C. Lin, R. Xie, L. Li, Z. Huang, and T. Li. Premise: personalized news recommendation via implicit social experts. In X. wen Chen, G. Lebanon, H. Wang, and M. J. Zaki, editors, CIKM, pages 1607--1611. ACM, 2012. Google ScholarDigital Library
- J. Liu, P. Dolan, and E. R. Pedersen. Personalized news recommendation based on click behavior. In IUI. ACM, 2010. Google ScholarDigital Library
- M. Liu, R. Cai, M. Zhang, and L. Zhang. User browsing behavior-driven web crawling. In CIKM. ACM, 2011. Google ScholarDigital Library
- Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li. BrowseRank: letting web users vote for page importance. SIGIR, 2008. Google ScholarDigital Library
- Y. Liu, M. Zhang, S. Ma, and L. Ru. User Browsing Graph: Structure, Evolution and Application. In WSDM, 2009.Google Scholar
- Y. Lv, T. Moon, P. Kolari, Z. Zheng, X. Wang, and Y. Chang. Learning to model relatedness for news recommendation. In WWW. ACM, 2011. Google ScholarDigital Library
- R. M. C. McCreadie, C. Macdonald, and I. Ounis. News article ranking: leveraging the wisdom of bloggers. In RIAO, 2010. Google ScholarDigital Library
- A. L. Montgomery, S. Li, K. Srinivasan, and J. C. Liechty. Modeling online browsing and path analysis using clickstream data. Marketing Science, 23(4):579--595, 2004. Google ScholarDigital Library
- A. M. Rashid, G. Karypis, and J. Riedl. Learning Preferences of New Users in Recommender Systems: An Information Theoretic Approach. In ACM SIGKDD Explorations Newsletter, volume 10, page 90, Dec. 2008. Google ScholarDigital Library
- A. Said and A. Bellogín. News Recommendation in the Wild: Recommendation Algorithms in the NRS Challenge. 2013.Google Scholar
- G. Shaw, Y. Xu, and S. Geva. Using Association Rules to Solve the Cold-Start Problem in Recommender Systems. In Advances in Knowledge Discovery and Data..., pages 21--24, 2010. Google ScholarDigital Library
- H. Sobhanam and a. K. Mariappan. Addressing cold start problem in recommender systems using association rules and clustering technique. In ICCCI, pages 1--5. Ieee, Jan. 2013.Google ScholarCross Ref
- A. Spink, M. Park, B. J. Jansen, and J. Pedersen. Multitasking during web search sessions. Inf. Process. Manage., 42(1):264--275, 2006. Google ScholarDigital Library
- I. Trajkovski. Pagerank-Like Algorithm for Ranking News Stories and News Portals. ICT Innovations, 231:87--96, 2013.Google Scholar
- M. Trevisiol, L. Chiarandini, L. M. Aiello, and A. Jaimes. In SIGIR. ACM, 2012.Google Scholar
- M. Tsagkias and R. Blanco. Language intent models for inferring user browsing behavior. In SIGIR, page 335, New York, New York, USA, 2012. ACM Press. Google ScholarDigital Library
- M. Tsagkias and R. Blanco. Language intent models for inferring user browsing behavior. In SIGIR. ACM, 2012. Google ScholarDigital Library
- M. Tsagkias, M. de Rijke, and W. Weerkamp. Linking online news and social media. In WSDM. ACM, 2011. Google ScholarDigital Library
- W. Wagner. Steven bird, ewan klein and edward loper: Natural language processing with python, analyzing text with natural language toolkit. Lang. Resour. Eval., 44(4):421--424, 2010. Google ScholarDigital Library
- C. Wang, M. Zhang, L. Ru, and S. Ma. Automatic online news topic ranking using media focus and user attention based on aging theory. In CIKM. ACM, 2008. Google ScholarDigital Library
- X. Yang, Y. Guo, and Y. Liu. Bayesian-inference based recommendation in online social networks. In IEEE INFOCOM, pages 551--555. Ieee, Apr. 2011.Google ScholarCross Ref
Index Terms
- Cold-start news recommendation with domain-dependent browse graph
Recommendations
Item cold-start recommendations: learning local collective embeddings
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systemsRecommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the ...
Improving Cold Start Recommendation by Mapping Feature-Based Preferences to Item Comparisons
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationMany Recommender Systems (RSs) rely on user preference data in the form of ratings or likes for items. Previous research has shown that item comparisons can also be effectively used to model user preferences and build RS. However, users often express ...
N-dimensional Markov random field prior for cold-start recommendation
A recommender system is a commonly used technique to improve user experience in e-commerce applications. One of the popular recommender methods is Matrix Factorization (MF) that learns the latent profile of both users and items. However, if the ...
Comments