In: Proceedings of the 18th international conference on World wide web. In: Proceedings of ACM SIGIR workshop on recommender systems, vol 60, CiteseerĬhu W, Park S-T (2009) Personalized recommendation on dynamic content using predictive bilinear models. Morgan Kaufmann Publishers Inc., pp 43–52Ĭlaypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M (1999) Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the fourteenth conference on Uncertainty in artificial intelligence. J Am Soc Inf Sci Technol 61(9):1853–1870īreese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Mach Learn 27(3):313–331Ĭota RG, Ferreira AA, Nascimento C, Gonçalves MA, Laender AHF (2010) An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. In: Proceedings of the third annual conference on Autonomous Agents. ACM, pp 158–166īillsus D, Pazzani MJ (1999) A personal news agent that talks, learns and explains. In: Proceedings of the 1st ACM conference on electronic commerce. Schafer JB, Konstan J, Riedi J (1999) Recommender systems in e-commerce. In: Proceedings of the sixth ACM international conference on Web search and data mining. Li L, Li T (2013) News recommendation via hypergraph learning: encapsulation of user behavior and news content. In: Proceedings of the 16th international conference on World Wide Web. ACM, pp 31–40ĭas AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In: Proceedings of the 15th international conference on Intelligent user interfaces. Liu J, Dolan P, Pedersen ER (2010) Personalized news recommendation based on click behavior. In: ACM conference on information retrieval (SIGIR) Li L, Wang DD, Li T, Knox D, Padmanabhan B (2011) Scene: a scalable two-stage personalized news recommendation system. Finally, we designed several experiments compared to the state-of-the-art approaches, and the experimental results show that our proposed method significantly improves the accuracy, diversity and NDCG metrics. In this way, we can provide the news articles for users and meet their requirement: after reading the recommended news, the user gains a better understanding of the progression of the news story they read before. At last, we use a greedy selection method for filtering the final recommended news articles with considering accuracy and diversity. Besides, we propose a method to construct a news story chain on a news corpus with date information. In this paper, we propose to define the quality of a news story chain. In other words, they fail to provide more useful information with considering the progression of the news story chain. In this way, they only considered the relationship between news articles and the users and ignored the context of news report background. Traditionally, most of recent researches use users’ reading history (content based) or access pattern (collaborative filtering based) to recommend newly published news to them. Previous methods strive to satisfy the users by constructing the users’ preference profiles. News personalized recommendation has long been a favorite research in recommender.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |