Semantic Context-Aware Recommendation via Topic Models Leveraging Linked Open Data

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10041
• 期：1
• 页码：263-277
• 全文大小：466 KB
• 参考文献：1.Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). doi:10.​1007/​3-540-48157-5_​29 CrossRef
  2.Aciar, S.: Mining context information from consumers reviews. In: Proceedings of Workshop on Context-Aware Recommender System, vol. 201. ACM (2010)
  3.Agarwal, D., Chen, B.C.: flDA: matrix factorization through latent Dirichlet allocation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 91–100. ACM (2010)
  4.Anand, S.S., Kearney, P., Shapcott, M.: Generating semantically enriched user profiles for web personalization. ACM Trans. Internet Technol. (TOIT) 7(4), 22 (2007)CrossRef
  5.Ardissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Appl. Artif. Intell. 17(8–9), 687–714 (2003)CrossRef
  6.Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Pers. Ubiquit. Comput. 16(5), 507–526 (2012)CrossRef
  7.Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 301–304. ACM (2011)
  8.Baltrunas, L., Ricci, F.: Context-based splitting of item ratings in collaborative filtering. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 245–248. ACM (2009)
  9.Beutel, A., Murray, K., Faloutsos, C., Smola, A.J.: CoBaFi: collaborative bayesian filtering. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 97–108. ACM (2014)
  10.Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. Semant. Serv. Interoperability Web Appl. Emerg. Concepts 5, 205–227 (2009)
  11.Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia-a crystallization point for the web of data. Web Semant. Sci. Serv. Agents World Wide Web 7(3), 154–165 (2009)CrossRef
  12.Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
  13.Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)
  14.Cantador, I., Bellogín, A., Castells, P.: A multilayer ontology-based hybrid recommendation model. AI Commun. 21, 203–210 (2008)MathSciNet MATH
  15.Cantador, I., Castells, P.: Semantic contextualisation in a news recommender system. In: Workshop on Context-Aware Recommender Systems (CARS 2009) (2009)
  16.Chen, G., Chen, L.: Recommendation based on contextual opinions. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 61–73. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-08786-3_​6
  17.Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a context-aware electronic tourist guide: some issues and experiences. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 17–24. ACM (2000)
  18.Di Noia, T., Mirizzi, R., Ostuni, V.C., Romito, D., Zanker, M.: Linked open data to support content-based recommender systems. In: Proceedings of the 8th International Conference on Semantic Systems, pp. 1–8. ACM (2012)
  19.Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. USA 101(Suppl 1), 5228–5235 (2004)CrossRef
  20.Hariri, N., Mobasher, B., Burke, R.: Query-driven context aware recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 9–16. ACM (2013)
  21.Hariri, N., Zheng, Y., Mobasher, B., Burke, R.: Context-aware recommendation based on review mining. General Co-Chairs, p. 27 (2011)
  22.Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: a spatially and temporally enhanced knowledge base from Wikipedia. In: Proceedings of the Twenty-Third international Joint Conference on Artificial Intelligence, pp. 3161–3165. AAAI Press (2013)
  23.Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM (1999)
  24.Jahrer, M., Töscher, A., Legenstein, R.: Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 693–702. ACM (2010)
  25.Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 79–86. ACM (2010)
  26.Leskovec, J., McAuley, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. Department of Computer Science, Stanford University (2013)
  27.Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296. ACM (2011)
  28.Meyers, O.C.: A mood-based music classification and exploration system. Ph.D. thesis, Massachusetts Institute of Technology (2007)
  29.Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. In: Berendt, B., Hotho, A., Mladenič, D., Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, pp. 57–76. Springer, Heidelberg (2004). doi:10.​1007/​978-3-540-30123-3_​4 CrossRef
  30.Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 85–92. ACM (2013)
  31.Passant, A.: dbrec — music recommendations using DBpedia. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6497, pp. 209–224. Springer, Heidelberg (2010). doi:10.​1007/​978-3-642-17749-1_​14 CrossRef
  32.Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
  33.Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning, pp. 880–887. ACM (2008)
  34.Semeraro, G., Lops, P., Basile, P., de Gemmis, M.: Knowledge infusion into content-based recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 301–304. ACM (2009)
  35.Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456. ACM (2011)
  36.Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A temporal context-aware model for user behavior modeling in social media systems. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1543–1554. ACM (2014)
  37.Yin, H., Cui, B., Chen, L., Hu, Z., Zhou, X.: Dynamic user modeling in social media systems. ACM Trans. Inf. Syst. (TOIS) 33(3), 10 (2015)CrossRef
  38.Yin, H., Cui, B., Sun, Y., Hu, Z., Chen, L.: LCARS: a spatial item recommender system. ACM Trans. Inf. Syst. (TOIS) 32(3), 11 (2014)CrossRef
  39.Yin, H., Zhou, X., Shao, Y., Wang, H., Sadiq, S.: Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1631–1640. ACM (2015)
  
• 作者单位：Mehdi Allahyari (19)
  Krys Kochut (19)
  
  19. Computer Science Department, University of Georgia, Athens, Georgia, USA
  
• 丛书名：Web Information Systems Engineering ¨C WISE 2016
• ISBN：978-3-319-48740-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10041

文摘

Context aware recommendation systems are used to provide personalized recommendations by exploiting contextual situation. They take into account not only user preferences, but also additional relevant information (context). Statistical topic models such as Latent Dirichlet Allocation (LDA) have been extensively used for discovering latent semantic topics in text documents. In this paper, we propose a probabilistic topic model that incorporates user interests, item representation and context information in a single framework. In our approach, the contextual information is represented as a subset of the items feature space which is acquired from the knowledge available in the Linked Open Data (LOD). We use DBpedia, a well-known knowledge base in LOD, to utilize the context information in recommendation. Our proposed recommendation framework computes the conditional probability of each item given the user preferences and the additional context. We use these probabilities as recommendation scores to find top-n items for recommendations. The performed experiments demonstrate the effectiveness of our proposed method and shows that leveraging semantic context from the Linked Open Data can improve the quality of the recommendations.