Skip to main content
Springer Nature Link
Log in

Asymmetric response aggregation heuristics for rating prediction and recommendation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

User-based collaborative filtering is widely used in recommendation systems, which normally comprises three steps: (1) finding the nearest conceptual neighbors, (2) aggregating the neighbors’ ratings to predict the ratings of unrated items, and (3) generating recommendations based on the prediction. Existing algorithms mainly focus on steps 1 and 3 but neglect subtle treatments of aggregating neighbors’ suggestions in step 2. Based on the discovery of psychology that (i) users’ responses to positive and negative suggestions are different, and (ii) users may respond differently from one another, this paper proposes a Personal Asymmetry Response-based Suggestions Aggregation (PARSA) algorithm, which first uses a linear regression method to learn each user’s response to negative/positive suggestions from neighbors and then uses a gradient descent algorithm for optimizing them. In addition, this paper designs an Identical Asymmetry Response-based Suggestions Aggregation (IARSA) baseline algorithm, which assumes that all the users’ responses to suggestions are identical as references to verify the key contribution of the heuristics employed in our PARSA algorithm that user may responses differently to positive and negative suggestions. Three sets of experiments are designed and implemented over two real-life datasets (i.e., Eachmovie and Netflix) to evaluate the performance of our algorithms. Further, in order to eliminate the influence of different similarity measures, this paper selects three kinds of similarity measures to discover neighbors. Experimental results demonstrate that most people indeed pay more attention to negative suggestions and our algorithms achieve better prediction and recommendation performances than the compared algorithms under various similarity measures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Teng Y, He L (2012, April) Rating prediction algorithm and recommendation based on user behavior in IPTV. In: 2012 2nd International conference on consumer electronics, communications and networks (CECNet). IEEE, pp. 3373–3378

  2. Ye L, Wu C, L Mi (2017) Collaborative filtering recommendation based on trust model with fused similar factor. In: MATEC Web of Conferences. Vol. 139, pp. 00010

  3. Anderson NH (1974) Cognitive algebra: integration theory applied to social attribution. In: Advances in experimental social psychology, vol 7. Academic, Amsterdam, pp 1–101

    Google Scholar 

  4. Frodi AM, Lamb ME, Leavitt LA, Donovan WL (1978) Fathers’ and mothers’ responses to infant smiles and cries. Infant Behav Dev 1:187–198

    Article  Google Scholar 

  5. Fiske ST (1980) Attention and weight in person perception: the impact of negative and extreme behavior. J Pers Soc Psychol 38(6):889–906

    Article  Google Scholar 

  6. Hamilton DL, Zanna MP (1972) Differential weighting of favorable and unfavorable attributes in impressions of personality. J Res Pers 6:2–3

    Google Scholar 

  7. Pratto F, John OP (1991) Automatic vigilance: the attention-grabbing power of negative social information. J Pers Soc Psychol 61(3):380–391

    Article  Google Scholar 

  8. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  9. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

    Article  Google Scholar 

  10. Shi Y, Larson M, Hanjalic A (2009, October) Exploiting user similarity based on rated-item pools for improved user-based collaborative filtering. In: Proceedings of the third ACM conference on recommender systems. ACM, pp. 125–132

  11. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th international conference on World Wide Web 1:285–295

    Google Scholar 

  12. Li W, Xu H, Ji M, Xu Z, Fang H (2016, October) A hierarchy weighting similarity measure to improve user-based collaborative filtering algorithm. In: 2016 2nd IEEE International conference on computer and communications (ICCC). IEEE, pp. 843–846

  13. Azadjalal MM, Moradi P, Abdollahpouri A, Jalili M (2017) A trust-aware recommendation method based on Pareto dominance and confidence concepts. Knowl-Based Syst 116:130–143

    Article  Google Scholar 

  14. Frolov E, Oseledets I (2016, September) Fifty shades of ratings: how to benefit from a negative feedback in top-N recommendations tasks. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp. 91–98

  15. Hu Y, Shi W, Li H, Hu X (2017) Mitigating data sparsity using similarity reinforcement-enhanced collaborative filtering. ACM Transactions on Internet Technology (TOIT) 17(3):31

    Article  Google Scholar 

  16. Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56:156–166

    Article  Google Scholar 

  17. Li, Q., Lin, Z., & Fei, Z. (2016). Similarity coefficient of collaborative filtering based on contribution of neighbors. In: 2016 International conference on machine learning and cybernetics (ICMLC), Vol. 1. IEEE, pp. 226–232

  18. Lee WP, Ma CY (2016) Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowl-Based Syst 106:125–134

    Article  Google Scholar 

  19. Mahara T (2016) A new similarity measure based on mean measure of divergence for collaborative filtering in sparse environment. Proc Comput Sci 89:450–456

    Article  Google Scholar 

  20. Srikanth T, Shashi M (2015) A new similarity measure for user-based collaborative filtering in recommender systems. Int J Comput Technol 14:6118–6128

    Article  Google Scholar 

  21. Wu X, Cheng B, Chen J (2015) Collaborative filtering service recommendation based on a novel similarity computation method. IEEE Trans Serv Comput 10(3):352–365

    Article  Google Scholar 

  22. Wang Y, Deng J, Gao J, Zhang P (2017) A hybrid user similarity model for collaborative filtering. Inf Sci 418:102–118

    Article  Google Scholar 

  23. Zang X, Liu T, Qiao S, Gao W, Wang J, Sun X, Zhang B (2016, June) A new weighted similarity method based on neighborhood user contributions for collaborative filtering. In: 2016 IEEE first international conference on data science in cyberspace (DSC). IEEE, pp. 376–381

  24. Zeng W, Shang MS (2010). Effects of negative ratings on personalized recommendation. In: 2010 5th International conference on computer science & education. IEEE, pp. 375–379

  25. Liang LI, Yu-Xin D, Chun-Hui Z, Wei-Jie C (2017) Collaborative filtering recommendation algorithm combined with user trust. J Chn Comput Syst 38(5):951–955

    Google Scholar 

  26. Lu K, Xie L, Li MC (2016) Research on implied-trust aware collaborative filtering recommendation algorithm. J Chn Comput Syst 37(2):241–245

    Google Scholar 

  27. Costa PT Jr, Terracciano A, McCrae RR (2001) Gender differences in personality traits across cultures: robust and surprising findings. J Pers Soc Psychol 81(2):322

    Article  Google Scholar 

  28. Wang X, Ji SJ, Liang YQ, Leung HF, Chiu DK (2019) An unsupervised strategy for defending against multifarious reputation attacks. Appl Intell 49:4189–4210

    Article  Google Scholar 

  29. Wang J, Wen Y, Gou Y, Ye Z, Chen H (2017) Fractional-order gradient descent learning of BP neural networks with Caputo derivative. Neural Netw 89:19–30

    Article  Google Scholar 

  30. Wu W, Zhao J, Zhang C, Meng F, Zhang Z, Zhang Y, Sun Q (2017) Improving performance of tensor-based context-aware recommenders using Bias tensor factorization with context feature auto-encoding. Knowl-Based Syst 128:71–77

    Article  Google Scholar 

  31. Xu XY, Liu JM (2016) Collaborative filtering recommendation algorithm based on multi-level item similarity. Comput Sci 34:262–265

    Google Scholar 

  32. Chao C, Qu S, Du T (2014) Research of collaborative filtering recommendation algorithm for short text. J Comput Commun 2(14):59

    Article  Google Scholar 

  33. Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web. ACM, pp. 22–32

  34. Ge M, Delgado-Battenfeld C, Jannach D (2010, September) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp. 257–260

  35. Yera R, Martinez L (2017) Fuzzy tools in recommender systems: a survey. Int J Computation Intell Syst 10(1):776–803

    Article  Google Scholar 

  36. Chiu DK, Leung HF, Lam KM (2009) On the making of service recommendations: an action theory based on utility, reputation, and risk attitude. Expert Syst Appl 36(2):3293–3301

    Article  Google Scholar 

  37. Ji SJ, Ma HY, Zhang SL, Leung HF, Chiu D, Zhang CJ, Fang XW (2016) A pre-evolutionary advisor list generation strategy for robust defensing reputation attacks. Knowl-Based Syst 103:1–18

    Article  Google Scholar 

  38. Ji S, Ma H, Liang Y, Leung H, Zhang C (2017) A whitelist and blacklist-based co-evolutionary strategy for defensing against multifarious trust attacks. Appl Intell 47(4):1115–1131

    Article  Google Scholar 

  39. Hung PC, Chiu DK, Fung WW, Cheung WK, Wong R, Choi SP, Cheng VS (2007) End-to-end privacy control in service outsourcing of human intensive processes: a multi-layered web service integration approach. Inf Syst Front 9(1):85–101

    Article  Google Scholar 

  40. Chiu DK, Lin DT, Kafeza E, Wang M, Hu H, Hu H, Zhuang Y (2010) Alert based disaster notification and resource allocation. Inf Syst Front 12(1):29–47

    Article  Google Scholar 

  41. Meng S, Chiu DK, Kafeza E, Wenyin L, Li Q (2010) Automated management of assets based on RFID triggered alarm messages. Inf Syst Front 12(5):563–578

    Article  Google Scholar 

  42. Qiu LQ, Jia W, Yu J, Fan X, Gao W(2019) PHG: A Three-Phase Algorithm for Influence Maximization Based on Community Structure, IEEE Access, 7: 62511-62522

Download references

Acknowledgements

This paper is supported in part by the Natural Science Foundation of China (no. 71772107, 71,403,151, 61,502,281, 61,433,012, U1435215), Qingdao social science planning project (no. QDSKL1801138), the National key R&D Plan (no. 2018YFC0831002), the key R&D Plan of Shandong Province (no.2018GGX101045), humanity and social science Fund of the Ministry of education (no. 18YJAZH136), the Natural Science Foundation of Shandong Province (Nos. ZR2018BF013, ZR2019MF003, ZR2013FM023), the innovative Research Foundation of Qingdao (grant no. 18–2–2-41-jch), Shandong education quality improvement plan for postgraduate, the leading talent development program of Shandong University of Science and Technology, special funding for Taishan scholar construction project, and SDUST research fund

Author information

Authors and Affiliations

  1. Key Laboratory for wisdom mine information technology of Shandong Province, Shandong University of Science and Technology, Qingdao, China

    Shujuan Ji, Wei Yang, Shenghui Guo & Xinyue Yuan

  2. Faculty of Education, The University of Hong Kong, Hong Kong, China

    Dickson K.W. Chiu

  3. Network Information Center (NIC), Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Chunjin Zhang

Authors
  1. Shujuan Ji
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wei Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shenghui Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Dickson K.W. Chiu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Chunjin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xinyue Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chunjin Zhang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In order to get optimal TMFSF similarity, we use an interpolation method to get the optimal value of f over two datasets, which is assigned a value of 1.0, 1.1, 1.2, 1.3, 1.4, and 1.5, respectively. We employ MAE and RMSE as evaluation criteria in this experiment. The smaller the values of MAE and RMSE, the more accurate the prediction is.

For the Eachmovie dataset, we obtain the MAE and RMSE values as shown in Fig. 10a and b, when f ranges from 1.0 to 1.5. Then we can see that, when f is 1.0, the values of MAE and RMSE achieve their minimums respectively. That means, the optimal value of f is 1.0. Similarly, as shown Fig. 11a and b, we obtain the optimal MAE and RMSE values on the Netflix dataset when f is 1.1. Therefore, we can conclude that the optimal values of f on the two datasets are 1.0 and 1.1, respectively

Fig. 10
figure 10

The value of MAE and RMSE on Eachmovie (a) MAE (b) RMSE

Full size image
Fig. 11
figure 11

The value of MAE and RMSE on Netflix (a) MAE (b) RMSE

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, S., Yang, W., Guo, S. et al. Asymmetric response aggregation heuristics for rating prediction and recommendation. Appl Intell 50, 1416–1436 (2020). https://doi.org/10.1007/s10489-019-01594-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01594-2

Keywords