Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 375-396.doi: 10.1007/s11390-020-0135-9

Special Issue: Surveys; Data Management and Data Mining

• Regular Paper • Previous Articles     Next Articles

Serendipity in Recommender Systems: A Systematic Literature Review

Reza Jafari Ziarani and Reza Ravanmehr*   

  1. Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1477893855, Iran
  • Received:2019-10-28 Revised:2020-07-27 Online:2021-03-05 Published:2021-04-01
  • Contact: Reza Ravanmehr E-mail:r.ravanmehr@iauctb.ac.ir
  • About author:Reza Jafari Ziarani received his B.Sc. degree in hardware engineering from West Tehran Branch, Islamic Azad University, Tehran, in 2017, and his M.Sc. degree in software engineering from Central Tehran Branch, Islamic Azad University, Tehran, in 2019. His research interests include recommender systems, serendipity, and social network analysis.

A recommender system is employed to accurately recommend items, which are expected to attract the user's attention. The over-emphasis on the accuracy of the recommendations can cause information over-specialization and make recommendations boring and even predictable. Novelty and diversity are two partly useful solutions to these problems. However, novel and diverse recommendations cannot merely ensure that users are attracted since such recommendations may not be relevant to the user's interests. Hence, it is necessary to consider other criteria, such as unexpectedness and relevance. Serendipity is a criterion for making appealing and useful recommendations. The usefulness of serendipitous recommendations is the main superiority of this criterion over novelty and diversity. The bulk of studies of recommender systems have focused on serendipity in recent years. Thus, a systematic literature review is conducted in this paper on previous studies of serendipity-oriented recommender systems. Accordingly, this paper focuses on the contextual convergence of serendipity definitions, datasets, serendipitous recommendation methods, and their evaluation techniques. Finally, the trends and existing potentials of the serendipity-oriented recommender systems are discussed for future studies. The results of the systematic literature review present that the quality and the quantity of articles in the serendipity-oriented recommender systems are progressing.

Key words: systematic literature review; recommender system; serendipity; evaluation metric; evaluation method;

[1] Ricci F, Rokach L, Shapira B. Recommender systems:Introduction and challenges. In Recommender Systems Handbook (2nd edition), Ricci F, Rokach L, Shapira B (eds.), Springer, 2015, pp.1-34. DOI:10.1007/978-1-4899-7637-61.
[2] Sarwar B M, Karypis G, Konstan J A, Riedl J. Item-based collaborative filtering recommendation algorithms. In Proc. the 10th International Conference on World Wide Web, May 2001, pp.285-295. DOI:10.1145/371920.372071.
[3] Bhandari U, Sugiyama K, Datta A, Jindal R. Serendipitous recommendation for mobile apps using item-item similarity graph. In Proc. the 9th Asia Information Retrieval Symposium, December 2013, pp.440-451. DOI:10.1007/978-3-642-45068-638.
[4] Wan L, Yuan Y, Xia F, Liu H. To your surprise:Identifying serendipitous collaborators. IEEE Transactions on Big Data. DOI:10.1109/TBDATA.2019.2921567.
[5] Jenders M, Lindhauer T, Kasneci G, Krestel R, Naumann F. A serendipity model for news recommendation. In Proc. the 38th Annual German Conference on Artificial Intelligence, September 2015, pp.111-123. DOI:10.1007/978-3-319-24489-19.
[6] Alhijawi B, Obeid N, Awajan A, Tedmori S. Improving collaborative filtering recommender systems using semantic information. In Proc. the 9th International Conference on Information and Communication Systems, April 2018, pp.127-132. DOI:10.1109/iacs.2018.8355454.
[7] Castells P, Hurley N J, Vargas S. Novelty and diversity in recommender systems. In Recommender Systems Handbook (2nd edition), Ricci F, Rokach L, Shapira B (eds.), Springer, 2015, pp.881-918. DOI:10.1007/978-1-4899-7637-626.
[8] Karimi M, Jannach D, Jugovac M. News recommender systems-Survey and roads ahead. Information Processing & Management, 2018, 54(6):1203-1227. DOI:10.1016/j.ipm.2018.04.008.
[9] Avazpour I, Pitakrat T, Grunske L, Grundy J. Dimensions and metrics for evaluating recommendation systems. In Recommendation Systems in Software Engineering, Robillard M P, Maalej W, Walker R, Zimmermann T (eds.), Springer, 2014, pp.245-273. DOI:10.1007/978-3-642-45135-510.
[10] Wang C D, Deng Z H, Lai J H, Yu P S. Serendipitous recommendation in ecommerce using innovator-based collaborative filtering. IEEE Transactions on Cybernetics, 2019, 49(7):2678-2692. DOI:10.1109/tcyb.2018.2841924.
[11] Kotkov D, Wang S, Veijalainen J. A survey of serendipity in recommender systems. Knowledge-Based Systems, 2016, 111:180-192. DOI:10.1016/j.knosys.2016.08.014.
[12] Kaminskas M, Bridge D. Diversity, serendipity, novelty, and coverage:A survey and empirical analysis of beyondaccuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems, 2017, 7(1):Article No. 21-42. DOI:10.1145/2926720.
[13] Björneborn L. Three key affordances for serendipity:Toward a framework connecting environmental and personal factors in serendipitous encounters. Journal of Documentation, 2017, 73(5):1053-1081. DOI:10.1108/jd-07-2016-0097.
[14] Loewenstein G. The psychology of curiosity:A review and reinterpretation. Psychological Bulletin, 1994, 116(1):75-98. DOI:https://doi.org/10.1037/0033-2909.116.1.75.
[15] E Cunha M P, Rego A, Clegg S, Lindsay G. The dialectics of serendipity. European Management Journal, 2015, 33(1):9-18. DOI:10.1016/j.emj.2014.11.001.
[16] Sugiyama K, Kan M Y. Towards higher relevance and serendipity in scholarly paper recommendation. ACM SIGWEB Newsletter, 2015, Winter:Article No. 4. DOI:10.1145/2719943.2719947.
[17] McCay-Peet L, Toms E G, Kelloway E K. Examination of relationships among serendipity, the environment, and individual differences. Information Processing & Management, 2015, 51(4):391-412. DOI:10.1016/j.ipm.2015.02.004.
[18] Iaquinta L, de Gemmis M, Lops P, Semeraro G, Filannino M, Molino P. Introducing serendipity in a contentbased recommender system. In Proc. the 8th International Conference on Hybrid Intelligent Systems, September 2008, pp.168-173. DOI:10.1109/his.2008.25.
[19] Niu X, Abbas F, Maher M L, Grace K. Surprise me if you can:Serendipity in health information. In Proc. the 2018 CHI Conference on Human Factors in Computing Systems, April 2018, Article No. 23. DOI:10.1145/3173574.3173597.
[20] Yaqub O. Serendipity:Towards a taxonomy and a theory. Research Policy, 2018, 47(1):169-179. DOI:10.1016/j.respol.2017.10.007.
[21] Manca M, Boratto L, Carta S. Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system. Information Systems Frontiers, 2018, 20(4):825-839. DOI:10.1007/s10796-015-9600-3.
[22] Makri S, Blandford A, Woods M, Sharples S, Maxwell D. "Making my own luck":Serendipity strategies and how to support them in digital information environments. Journal of the Association for Information Science and Technology, 2014, 65(11):2179-2194. DOI:10.1002/asi.23200.
[23] Yu H, Wang Y, Fan Y, Meng S, Huang R. Accuracy is not enough:Serendipity should be considered more. In Proc. the 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, July 2017, pp.231-241. DOI:10.1007/978-3-319-61542-422.
[24] Khoshahval S, Farnaghi M, Taleai M, Mansourian A. A personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns. In Proc. the 21st AGILE International Conference on Geographic Information Science, June 2018, pp.271-289. DOI:10.1007/978-3-319-78208-914.
[25] Zuva K, Zuva T. Diversity and serendipity in recommender systems. In Proc. the Int. Conf. Big Data and Internet of Thing, December 2017, pp.120-124. DOI:10.1145/3175684.3175694.
[26] McCay-Peet L, Toms E G, Quan-Haase A. SEADE Workshop proposal-The serendipity factor:Evaluating the affordances of digital environments. In Proc. the 2016 ACM Conf. Human Information Interaction and Retrieval, March 2016, pp.341-343. DOI:10.1145/2854946.2878739.
[27] Nguyen T T, Harper F M, Terveen L, Konstan J A. User personality and user satisfaction with recommender systems. Information Systems Frontiers, 2018, 20(6):1173-1189. DOI:10.1007/s10796-017-9782-y.
[28] Khusro S, Ali Z, Ullah I. Recommender systems:Issues, challenges, and research opportunities. In Information Science and Applications, Kim K J, Joukov N (eds.), Springer, 2016, pp.1179-1189. DOI:10.1007/978-981-10-0557-2112.
[29] Gras B, Brun A, Boyer A. Identifying grey sheep users in collaborative filtering:A distribution-based technique. In Proc. the 2016 Conference on User Modeling Adaptation and Personalization, July 2016, pp.17-26. DOI:10.1145/2930238.2930242.
[30] Zheng Y, Agnani M, Singh M. Identification of grey sheep users by histogram intersection in recommender systems. In Proc. the 13th International Conference on Advanced Data Mining and Applications, November 2017, pp.148-161. DOI:10.1007/978-3-319-69179-411.
[31] Kotkov D, Konstan J A, Zhao Q, Veijalainen J. Investigating serendipity in recommender systems based on real user feedback. In Proc. the 33rd Annual ACM Symposium on Applied Computing, April 2018, pp.1341-1350. DOI:10.1145/3167132.3167276.
[32] Kitchenham B A, Charters S. Guidelines for performing systematic literature reviews in software engineering. Technical Report, Keele University and Durham University, 2007. https://userpages.uni-koblenz.de/~laemmel/esecourse/slides/slr.pdf, March 2020.
[33] Champiri Z D, Shahamiri S R, Salim S S B. A systematic review of scholar context-aware recommender systems. Expert Systems with Applications, 2015, 42(3):1743-1758. DOI:10.1016/j.eswa.2014.09.017.
[34] Figueroa C, Vagliano I, Rocha O R, Morisio M. A systematic literature review of Linked Data-based recommender systems. Concurrency and Computation:Practice and Experience, 2015, 27(17):4659-4684. DOI:10.1002/cpe.3449.
[35] Nunes I, Jannach D. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction, 2017, 27(3/4/5):393-444. DOI:10.1007/s11257-017-9195-0.
[36] Kitchenham B A, Budgen D, Brereton O P. The value of mapping studies-A participant-observer case study. In Proc. the 14th International Conference on Evaluation and Assessment in Software Engineering, April 2010, pp.25-33. DOI:10.14236/ewic/ease2010.4.
[37] Petersen K, Vakkalanka S, Kuzniarz L. Guidelines for conducting systematic mapping studies in software engineering:An update. Information and Software Technology, 2015, 64:1-18. DOI:10.1016/j.infsof.2015.03.007.
[38] Taramigkou M, Bothos E, Apostolou D, Mentzas G. Fostering serendipity in online information systems. In Proc. the 2013 IEEE International Technology Management Conference & International Conference on Engineering, Technology and Innovation, June 2013, pp.1-10. DOI:10.1109/itmc.2013.7352707.
[39] Wang M, Kawamura T, Sei Y, Nakagawa H, Tahara Y, Ohsuga A. Context-aware music recommendation with serendipity using semantic relations. In Proc. the 3rd Joint International Conference on Semantic Technology, May 2014, pp.17-32. DOI:10.1007/978-3-319-06826-82.
[40] Wong C C, Alias E S, Kishigami J. Playlist environmental analysis for the serendipity-based data mining. In Proc. the 2013 International Conference on Informatics, Electronics & Vision, 2013, pp.1-6. DOI:10.1109/iciev.2013.6572614.
[41] Ito H, Yoshikawa T, Furuhashi T. A study on improvement of serendipity in item-based collaborative filtering using association rule. In Proc. the 2014 IEEE International Conference on Fuzzy Systems, July 2014, pp.977-981. DOI:10.1109/fuzz-ieee.2014.6891655.
[42] de Pessemier T, Dooms S, Martens L. Comparison of group recommendation algorithms. Multimedia Tools and Applications, 2014, 72(3):2497-2541. DOI:10.1007/s11042-013-1563-0.
[43] Kito N, Oku K, Kawagoe K. Correlation analysis among the metadata-based similarity, acoustic-based distance, and serendipity of music. In Proc. the 19th International Database Engineering & Applications Symposium, July 2015, pp.198-199. DOI:10.1145/2790755.2790786.
[44] Rahman A, Wilson M L. Exploring opportunities to facilitate serendipity in search. In Proc. the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2015, pp.939-942. DOI:10.1145/2766462.2767783.
[45] Shah R, Patel A, Amin K. Item amalgamation approach for serendipity-oriented recommender system. In Proc. International Conference on ICT for Sustainable Development, July 2016, pp.397-404. DOI:10.1007/978-981-10-0135-239.
[46] de Gemmis M, Lops P, Semeraro G. Emotion detection techniques for the evaluation of serendipitous recommendations. In Emotions and Personality in Personalized Services-Models, Evaluation and Applications, Tkalčič M, De Carolis B, de Gemmis M et al. (eds.) Springer, 2016, pp.357-376. DOI:10.1007/978-3-319-31413-617.
[47] Chantanurak N, Punyabukkana P, Suchato A. Video recommender system using textual data:Its application on LMS and serendipity evaluation. In Proc. the 2016 IEEE Int. Conf. Teaching Assessment, and Learning for Engineering, Dec. 2016, pp.289-295. DOI:10.1109/tale.2016.7851809.
[48] Kotkov D, Wang S, Veijalainen J. Improving serendipity and accuracy in cross-domain recommender systems. In Proc. the 12th International Conference on Web Information Systems and Technologies, September 2017, pp.105-119. DOI:10.1007/978-3-319-66468-26.
[49] Karpus A, Vagliano I, Goczyla K. Serendipitous recommendations through ontology-based contextual pre-filtering. In Proc. the 13th International Conference on Beyond Databases, May 2017, pp.246-259. DOI:10.1007/978-3-319-58274-021.
[50] Eichler J S, Casanova M A, Furtado A L et al. Searching linked data with a twist of serendipity. In Proc. the 29th International Conference on Advanced Information Systems Engineering, June 2017, pp.495-510. DOI:10.1007/978-3-319-59536-831.
[51] Afridi A H. User control and serendipitous recommendations in learning environments. Procedia Computer Science, 2018, 130:214-221. DOI:10.1016/j.procs.2018.04.032.
[52] Khalili A, van den Besselaar P, de Graaf K A. FERASAT:A serendipity-fostering faceted browser for linked data. In Proc. the 15th International Conference on Semantic Web, June 2018, pp.351-366. DOI:10.1007/978-3-319-93417-423.
[53] Huang J, Ding S, Wang H, Liu T. Learning to recommend related entities with serendipity for web search users. ACM Transactions on Asian and Low-Resource Language Information Processing, 2018, 17(3):Article No. 25. DOI:10.1145/3185663.
[54] Grange C, Benbasat I, Burton-Jones A. With a little help from my friends:Cultivating serendipity in online shopping environments. Information & Management, 2019, 56(2):225-235. DOI:10.2139/ssrn.2993431.
[55] Amal S, Tsai C H, Brusilovsky P, Kuik T, Minkov E. Relational social recommendation:Application to the academic domain. Expert Systems with Applications, 2019, 124:182-195. DOI:10.1016/j.eswa.2019.01.061.
[56] Reviglio U. Serendipity as an emerging design principle of the infosphere:Challenges and opportunities. Ethics and Information Technology, 2019, 21(2):151-166. DOI:10.1007/s10676-018-9496-y.
[57] Pandey G, Kotkov D, Semenov A. Recommending serendipitous items using transfer learning. In Proc. the 27th ACM International Conference on Information and Knowledge Management, October 2018, pp.1771-1774. DOI:10.1145/3269206.3269268.
[58] Jain I, Hasija H. An effective approach for providing diverse and serendipitous recommendations. In Proc. the 3rd International Conference on Information Systems Design and Intelligent Applications, January 2016, pp.11-18. DOI:10.1007/978-81-322-2757-12.
[59] Aytekin T, Karakaya M. Clustering-based diversity improvement in top-N recommendation. Journal of Intelligent Information Systems, 2014, 42(1):1-18. DOI:10.1007/s10844-013-0252-9.
[60] Zheng Q, Chan C K, Ip H H. An unexpectedness-augmented utility model for making serendipitous recommendation. In Proc. the 15th Industrial Conference on Data Mining, July 2015, pp.216-230. DOI:10.1007/978-3-319-20910-416.
[61] De Gemmis M, Lops P, Semeraro G, Musto C. An investigation on the serendipity problem in recommender systems. Information Processing & Management, 2015, 51(5):695-717. DOI:10.1016/j.ipm.2015.06.008.
[62] Wu S, Guo W, Xu S, Huang Y, Wang L, Tan T. Coupled topic model for collaborative filtering with user-generated content. IEEE Transactions on Human-Machine Systems, 2016, 46(6):908-920. DOI:10.1109/thms.2016.2586480.
[63] Elmisery A M. Private personalized social recommendations in an IPTV system. New Review of Hypermedia and Multimedia, 2014, 20(2):145-167. DOI:10.1080/13614568.2014.889222.
[64] Adamopoulos P, Tuzhilin A. On unexpectedness in recommender systems:Or how to better expect the unexpected. ACM Transactions on Intelligent Systems and Technology, 2015, 5(4):Article No. 54. DOI:10.1145/2559952.
[65] Ziegler C N, Hornung T, Przyjaciel-Zablocki M, Gauß S, Lausen G. Music recommenders based on hybrid techniques and serendipity. Web Intelligence and Agent Systems:An International Journal, 2014, 12(3):235-248. DOI:10.3233/wia-140294.
[66] Meng Q, Hatano K. Visualizing basic words chosen by latent Dirichlet allocation for serendipitous recommendation. In Proc. the 3rd International Conference on Advanced Applied Informatics, August 2014, pp.819-824. DOI:10.1109/iiai-aai.2014.164.
[67] Qureshi M A, Greene D. Lit@EVE:Explainable recommendation based on Wikipedia concept vectors. In Proc. the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, September 2017, pp.409-413. DOI:10.1007/978-3-319-71273-441.
[68] Zolaktaf Z, AlOmei O, Pottinger R. Bridging the gap between user-centric and offline evaluation of personalized recommendation systems. In Proc. the 26th Conf. User Modeling, Adaptation and Personalization, July 2018, pp.183-186. DOI:10.1145/3213586.3226216.
[69] Andel P V. Anatomy of the unsought finding. Serendipity:Origin, history, domains, traditions, appearances, patterns and programmability. The British Journal for the Philosophy of Science, 1994, 45(2):631-648. DOI:10.1093/bjps/45.2.631.
[70] Geison G L. The Private Science of Louis Pasteur (1st edition). Princeton University Press, 1995.
[71] Afridi A H. Serendipitous recommenders for teachers in higher education. In Handbook of Research on Faculty Development for Digital Teaching and Learning, Elçi A, Beith L L, Elçi A(eds.), IGI Global, 2019, pp.333-353. DOI:10.4018/978-1-5225-8476-6.ch017.
[72] Niu X, Abbas F. A framework for computational serendipity. In Proc. the 25th Conference on User Modeling, Adaptation and Personalization, July 2017, pp.360-363. DOI:10.1145/3099023.3099097.
[73] Deng Z H, Huang L, Wang C D, Lai J H, Yu P S. DeepCF:A unified framework of representation learning and matching function learning in recommender system. In Proc. the 33rd AAAI Conference on Artificial Intelligence, 2019, pp.61-68. DOI:10.1609/aaai.v33i01.330161.
[74] He X, Liao L, Zhang H, Nie L, Hu X, Chua T S. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, April 2017, pp.173-182. DOI:10.1145/3038912.3052569.
[75] Maccatrozzo V, Terstall M, Aroyo L, Schreiber G. SIRUP:Serendipity in recommendations via user perceptions. In Proc. the 22nd International Conference on Intelligent User Interfaces, March 2017, pp.35-44. DOI:10.1145/3025171.3025185.
[76] Chang Y H, Tang M C. Serendipity with music streaming services:The mediating role of user and task characteristics. In Proc. the 13th International Conference on Information, March 2018, pp.435-441. DOI:10.1007/978-3-319-78105-148.
[77] Koster A, Koch F, Kim Y B. Serendipitous recommendation based on big context. In Proc. the 14th Ibero-American Conf. Artificial Intelligence, November 2014, pp.319-330. DOI:10.1007/978-3-319-12027-026.
[78] Zhou X, Xu Z, Sun X, Wang Q. A new information theory-based serendipitous algorithm design. In Proc. the 19th International Conference on Human Interface and the Management of Information, July 2017, pp.314-327. DOI:10.1007/978-3-319-58524-626.
[79] Kotkov D, Veijalainen J, Wang S. How does serendipity affect diversity in recommender systems? A serendipityoriented greedy algorithm. Computing, 2018, 102(2):393-411. DOI:10.1007/s00607-018-0687-5.
[80] Deshmukh A A, Nair P, Rao S. A scalable clustering algorithm for serendipity in recommender systems. In Proc. the 2018 IEEE International Conference on Data Mining Workshops, November 2018, pp.1279-1288. DOI:10.1109/icdmw.2018.00182.
[81] Yang Y, Xu Y, Wang E, Han J, Yu Z. Improving existing collaborative filtering recommendations via serendipitybased algorithm. IEEE Transactions on Multimedia, 2018, 20(7):1888-1900. DOI:10.1109/tmm.2017.2779043.
[82] Lu W, Chung F L. Computational creativity based video recommendation. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.793-796. DOI:10.1145/2911451.2914707.
[83] Magara M B, Ojo S O, Zuva T. Towards a serendipitous research paper recommender system using bisociative information networks (BisoNets). In Proc. the 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, Aug. 2018. DOI:10.1109/icabcd.2018.8465475.
[84] de Pessemier T, Vanhecke K, Martens L. A personalized and context-aware news offer for mobile devices. In Proc. the 11th International Conference on Web Information Systems and Technologies, May 2015, pp.147-168. DOI:10.1007/978-3-319-30996-58.
[85] Maake B M, Ojo S O, Zuva T. A serendipitous research paper recommender system. International Journal of Business and Management Studies, 2019, 11(1):39-53.
[86] Menk A, Sebastia L, Ferreira R. Curumim:A serendipitous recommender system based on human curiosity. Procedia Computer Science, 2017, 112:484-493. DOI:10.1016/j.procs.2017.08.098.
[87] Lambropoulos N, Fardoun H, Alghazzawi D M. Social networks serendipity for educational learning by surprise from big and small data analysis. In Proc. the 9th International Conference on Social Computing and Social Media, July 2017, pp.406-415. DOI:10.1007/978-3-319-58562-831.
[88] Afridi A H, Yasar A, Shakshuki E M. Facilitating research through serendipity of recommendations. Journal of Ambient Intelligence and Humanized Computing. DOI:10.1007/s12652-019-01354-7.
[89] Park D, Kim J, Sohn M. Serendipity-based recommendation framework for SNS users using tie strength and relation clustering. In Proc. the 13th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, July 2019, pp.636-645. DOI:10.1007/978-3-030-22263-560.
[90] Kitchenham B, Brereton O P, Budgen D, Turner M, Bailey J, Linkman S G. Systematic literature reviews in software engineering-A systematic literature review. Information and Software Technology, 2009, 51(1):7-15. DOI:10.1016/j.infsof.2008.09.009.
[91] Yamaba H, Tanoue M, Takatsuka K, Okazaki N, Tomita S. On a serendipity-oriented recommender system based on folksonomy and its evaluation. Procedia Computer Science, 2013, 22:276-284. DOI:10.1016/j.procs.2013.09.104.
[92] Sauer S, de Rijke M. Seeking serendipity:A living lab approach to understanding creative retrieval in broadcast media production. In Proc. the 39th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, July 2016, pp.989-992. DOI:10.1145/2911451.2914721.
[93] Chen L, Yang Y, Wang N, Yang K, Yuan Q. How serendipity improves user satisfaction with recommendations? A large-scale user evaluation. In Proc. the 2019 World Wide Web Conference, May 2019, pp.240-250. DOI:10.1145/3308558.3313469.
[94] Park J, Nam K. Group recommender system for store product placement. Data Mining and Knowledge Discovery, 2019, 33(1):204-229. DOI:10.1007/s10618-018-0600-z.
[95] Nishioka C, Hauk J, Scherp A. Towards serendipitous research paper recommender using tweets and diversification. In Proc. the 23rd International Conference on Theory and Practice of Digital Libraries, September 2019, pp.339-343. DOI:10.7717/peerj-cs.273.
[96] Xu Z, Yuan Y, Wei H, Wan L. A serendipity-biased Deepwalk for collaborators recommendation. PeerJ Computer Science, 2019, 5:Article No. e178. DOI:10.7717/peerjcs.178.
[97] Kim M C, Chen C. A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics, 2015, 104(1):239-263. DOI:10.1007/s11192-015-1595-5.
[98] Gunes I, Kaleli C, Bilge A, Polat H. Shilling attacks against recommender systems:A comprehensive survey. Artificial Intelligence Review, 2014, 42(4):767-799. DOI:10.1007/s10462-012-9364-9.
[99] Xi W D, Huang L, Wang C D, Zheng Y Y, Lai J. BPAM:Recommendation based on BP neural network with attention mechanism. In Proc. the 28th International Joint Conference on Artificial Intelligence, August 2019, pp.3906-3911. DOI:10.24963/ijcai.2019/542.
[1] Yu-Yao Liu, Bo Yang, Hong-Bin Pei, Jing Huang. Neural Explainable Recommender Model Based on Attributes and Reviews [J]. Journal of Computer Science and Technology, 2020, 35(6): 1446-1460.
[2] Yang Liu, Zhi Li, Wei Huang, Tong Xu, En-Hong Chen. Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation [J]. Journal of Computer Science and Technology, 2020, 35(2): 281-294.
[3] Qi Liu, Hong-Ke Zhao, Le Wu, Zhi Li, En-Hong Chen. Illuminating Recommendation by Understanding the Explicit Item Relations [J]. , 2018, 33(4): 739-755.
[4] Shi-Qi Shen, Yang Liu, Mao-Song. Optimizing Non-Decomposable Evaluation Metrics for Neural Machine Translation [J]. , 2017, 32(4): 796-804.
[5] Ming-Xin Gan, Lily Sun, Rui Jiang. Trinity: Walking on a User-Object-Tag Heterogeneous Network for Personalised Recommendations [J]. , 2016, 31(3): 577-594.
[6] Xin Xin, Chin-Yew Lin, Xiao-Chi Wei, He-Yan Huang. When Factorization Meets Heterogeneous Latent Topics: An Interpretable Cross-Site Recommendation Framework [J]. , 2015, 30(4): 917-932.
[7] Xiang-Liang Zhang, Tak Man Desmond Lee, and Georgios Pitsilis. Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering [J]. , 2013, 28(4): 616-624.
[8] Hui-Feng Sun, Jun-Liang Chen, Gang Yu, Chuan-Chang Liu, Yong Peng, Guang Chen, and Bo Cheng. JacUOD: A New Similarity Measurement for Collaborative Filtering [J]. , 2012, 27(6): 1252-1260.
[9] Marcelo G. Armentano, Daniela Godoy, and Analia Amandi. Topology-Based Recommendation of Users in Micro-Blogging Communities [J]. , 2012, 27(3): 624-634.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
[10] Li Minghui;. CAD System of Microprogrammed Digital Systems[J]. , 1987, 2(3): 226 -235 .

ISSN 1000-9000(Print)

         1860-4749(Online)
CN 11-2296/TP

Home
Editorial Board
Author Guidelines
Subscription
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
Tel.:86-10-62610746
E-mail: jcst@ict.ac.cn
 
  Copyright ©2015 JCST, All Rights Reserved