||Aciar, S., Zhang, D., Simoff, S., & Debenham, J. (2007). Informed recommender: Basing recommendations on consumer product reviews. IEEE Intelligent systems, 22(3). |
Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37-51.
Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: does it matter? Journal of health and social behavior, 1-10.
Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of marketing Research, 291-295.
Barragáns-Martínez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-López, M., Mikic-Fonte, F. A., & Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, 180(22), 4290-4311.
Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Paper presented at the Aaai/iaai.
Billsus, D., & Pazzani, M. J. (2000). User modeling for adaptive news access. User modeling and user-adapted interaction, 10(2-3), 147-180.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4), 331-370.
Burke, R. (2007). Hybrid Web Recommender Systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (pp. 377-408). Berlin, Heidelberg: Springer Berlin Heidelberg.
Cacheda, F., & Viña, Á. (2001). Understanding how people use search engines: a statistical analysis for e-business. Paper presented at the Proceedings of the e-Business and e-Work Conference and Exhibition.
Cheung, C. M., & Lee, M. K. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218-225.
Chu, W.-T., & Tsai, Y.-L. (2017). A hybrid recommendation system considering visual information for predicting favorite restaurants. World Wide Web, 1-19.
Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Paper presented at the Proceedings of the 12th international conference on World Wide Web.
Day, G. S. (1971). Attitude change, media and word of mouth. Journal of advertising research.
Desrosiers, C., & Karypis, G. (2011). A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook, 107-144.
Ebesu, T., & Fang, Y. (2017). Neural Semantic Personalized Ranking for item cold-start recommendation. Information Retrieval Journal, 20(2), 109-131.
Gao, H., Tang, J., & Liu, H. (2015). Addressing the cold-start problem in location recommendation using geo-social correlations. Data Mining and Knowledge Discovery, 29(2), 299-323.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of interactive marketing, 18(1), 38-52.
Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. Paper presented at the Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval.
Hsu, W.-C., & Chen, L.-C. (2017). A Novel Recommendation System for Dental Services Based on Online Word-of-Mouth. Information Resources Management Journal (IRMJ), 30(1), 30-47.
Huang, T. C.-K., Chen, Y.-L., & Chen, M.-C. (2016). A novel recommendation model with Google similarity. Decision Support Systems, 89, 17-27.
Kardan, A. A., & Ebrahimi, M. (2013). A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Information Sciences, 219, 93-110.
Kim, H.-N., El-Saddik, A., & Jo, G.-S. (2011). Collaborative error-reflected models for cold-start recommender systems. Decision Support Systems, 51(3), 519-531.
Kim, Y. S., Krzywicki, A., Wobcke, W., Mahidadia, A., Compton, P., Cai, X., & Bain, M. (2012). Hybrid techniques to address cold start problems for people to people recommendation in social networks. Paper presented at the Pacific Rim International Conference on Artificial Intelligence.
Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, 41(4), 2065-2073.
Lin, Z. (2014). An empirical investigation of user and system recommendations in e-commerce. Decision Support Systems, 68, 111-124.
Lops, P., De Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends Recommender systems handbook (pp. 73-105): Springer.
Mashal, I., Chung, T.-Y., & Alsaryrah, O. (2015). Toward service recommendation in Internet of Things. Paper presented at the Ubiquitous and Future Networks (ICUFN), 2015 Seventh International Conference on.
Melville, P., Mooney, R. J., & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. Paper presented at the Aaai/iaai.
Miao, Q., Li, Q., & Dai, R. (2009). AMAZING: A sentiment mining and retrieval system. Expert Systems with Applications, 36(3), 7192-7198.
Miranda, T., Claypool, M., Gokhale, A., Mir, T., Murnikov, P., Netes, D., & Sartin, M. (1999). Combining content-based and collaborative filters in an online newspaper. Paper presented at the In Proceedings of ACM SIGIR Workshop on Recommender Systems.
Mobasher, B., Jin, X., & Zhou, Y. (2004). Semantically enhanced collaborative filtering on the web Web Mining: From Web to Semantic Web (pp. 57-76): Springer.
Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial intelligence review, 13(5-6), 393-408.
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems The adaptive web (pp. 325-341): Springer.
Preisach, C., Marinho, L. B., & Schmidt-Thieme, L. (2010). Semi-supervised tag recommendation-using untagged resources to mitigate cold-start problems. Paper presented at the Pacific-Asia Conference on Knowledge Discovery and Data Mining.
Pu, P., Chen, L., & Hu, R. (2012). Evaluating recommender systems from the user’s perspective: survey of the state of the art. User modeling and user-adapted interaction, 22(4-5), 317-355.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. Paper presented at the Proceedings of the 1994 ACM conference on Computer supported cooperative work.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Paper presented at the Proceedings of the 10th international conference on World Wide Web.
Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. Paper presented at the Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval.
Smyth, B., & Cotter, P. (2000). A personalised TV listings service for the digital TV age. Knowledge-Based Systems, 13(2), 53-59.
Son, L. H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87-104.
Stratmann, W. C. (1975). A study of consumer attitudes about health care: the delivery of ambulatory services. Medical care, 537-548.
Wang, K.-Y., Ting, I.-H., & Wu, H.-J. (2013). Discovering interest groups for marketing in virtual communities: An integrated approach. Journal of business research, 66(9), 1360-1366.
Wattenbarger, D. W., Bailey, J. A., & Martinez, S. J. (1977). Interactive system for controlled vocabulary maintenance. Paper presented at the Proceedings of the 1977 annual conference.