Advertisement

User Modeling and User-Adapted Interaction

, Volume 29, Issue 3, pp 661–700 | Cite as

Subprofile-aware diversification of recommendations

  • Mesut KayaEmail author
  • Derek Bridge
Article

Abstract

A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to recommendation diversification uses re-ranking: the recommender system scores a set of candidate items for relevance to the user; it then re-ranks the candidates so that the subset that it will recommend achieves a balance between relevance and diversity. Ordinarily, we expect a trade-off between relevance and diversity: the diversity of the set of recommendations increases by including items that have lower relevance scores but which are different from the items already in the set. In early work, the diversity of a set of recommendations was given by the average of their distances from one another, according to some semantic distance metric defined on item features such as movie genres. More recent intent-aware diversification methods formulate diversity in terms of coverage and relevance of aspects. The aspects are most commonly defined in terms of item features. By trying to ensure that the aspects of a set of recommended items cover the aspects of the items in the user’s profile, the level of diversity is more personalized. In offline experiments on pre-collected datasets, intent-aware diversification using item features as aspects sometimes defies the relevance/diversity trade-off: there are configurations in which the recommendations exhibits increases in both relevance and diversity. In this paper, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification), and a variant called RSPAD (Relevance-based SPAD). In SPAD, the aspects are not item features; they are subprofiles of the user’s profile. We present and compare a number of different ways to extract subprofiles from a user’s profile. None of them is defined in terms of item features. Therefore, SPAD is useful even in domains where item features are not available or are of low quality. On three pre-collected datasets from three different domains (movies, music artists and books), we compare SPAD and RSPAD to intent-aware methods in which aspects are item features. We find on these datasets that SPAD and RSPAD suffer even less from the relevance/diversity trade-off: across all three datasets, they increase both relevance and diversity for even more configurations than other approaches to diversification. Moreover, we find that SPAD and RSPAD are the most accurate systems across all three datasets.

Keywords

Recommender systems Diversity Intent-aware diversification Subprofiles 

Notes

Acknowledgements

This paper emanates from research supported by a grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 which is co-funded under the European Regional Development Fund.

References

  1. Adomavicius, G., Kwon, Y.O.: Overcoming accuracy-diversity tradeoff in recommender systems: a variance-based approach. In: Proceedings of the 2008 Workshop on Information Technologies and Systems, pp. 151–156 (2008)Google Scholar
  2. Adomavicius, G., Kwon, Y.: Toward more diverse recommendations: item re-ranking methods for recommender systems. In: Proceedings of the 19th Workshop on Information Technologies and Systems, pp. 79–84 (2009)Google Scholar
  3. Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, pp. 5–14 (2009)Google Scholar
  4. Anelli, V.W., Bellini, V., Di Noia, T., La Bruna, W., Tomeo, P., Di Sciascio, E.: An analysis on time- and session-aware diversification in recommender systems. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, ACM, pp. 270–274 (2017)Google Scholar
  5. Antikacioglu, A., Ravi, R.: Post processing recommender systems for diversity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 707–716. ACM (2017)Google Scholar
  6. Bayer, I.: Fastfm: a library for factorization machines. (2015). arXiv preprint arXiv:1505.00641
  7. Bilgic, M., Mooney, R.J.: Explaining recommendations: satisfaction vs. promotion. In: Beyond Personalization Workshop, IUI, vol. 5 (2005)Google Scholar
  8. Bridge, D., Dunleavy, K.: If you liked Herlocker et al.’s explanations paper, then you might like this paper too. In: Joint Workshop on Interfaces and Human Decision Making in Recommender Systems (2014)Google Scholar
  9. Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 335–336. ACM (1998)Google Scholar
  10. Castells, P., Hurley, N.J., Vargas, S.: Novelty and diversity in recommender systems. In: Ricci, F., et al. (eds.) Recommender Systems Handbook, 2nd edn, pp. 881–918. Springer, New York (2015)CrossRefGoogle Scholar
  11. Cheng, P., Wang, S., Ma, J., Sun, J., Xiong, H.: Learning to recommend accurate and diverse items. In: Proceedings of the 26th International Conference on World Wide Web, pp. 183–192 (2017)Google Scholar
  12. Clarke, CLA., Kolla, M., Cormack, GV., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 659–666 (2008)Google Scholar
  13. Clements, M., de Vries, A.P., Reinders, M.J.: Optimizing single term queries using a personalized Markov random walk over the social graph. In: Workshop on Exploiting Semantic Annotations in Information Retrieval (2008)Google Scholar
  14. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)CrossRefGoogle Scholar
  15. Di Noia, T., Rosati, J., Tomeo, P., Di Sciascio, E.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. 382(C), 234–253 (2017)CrossRefGoogle Scholar
  16. Eskandanian, F., Mobasher, B., Burke, R.: A clustering approach for personalizing diversity in collaborative recommender systems. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 280–284, ACM (2017)Google Scholar
  17. Harper, F.M., Konstan, J.A.: The MovieLens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 19:1–19:19 (2015)CrossRefGoogle Scholar
  18. Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)CrossRefGoogle Scholar
  19. Hurley, N.J.: Personalised ranking with diversity. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 379–382 (2013)Google Scholar
  20. Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1), 2:1–2:42 (2016)CrossRefGoogle Scholar
  21. Kaya, M., Bridge, D.: Intent-aware diversification using item-based subprofiles. In: Tikk, D., Pu, P. (eds.) Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems, CEUR Workshop Proceedings, vol. 1905 (2017)Google Scholar
  22. Kaya, M., Bridge, D.: Accurate and diverse recommendations using item-based subprofiles. In: Proceedings of the 31th International Florida Artificial Intelligence Research Society Conference, pp. 462–467. AAAI (2018a)Google Scholar
  23. Kaya, M., Bridge, D.: Automatic playlist continuation using subprofile-aware diversification. In: Proceedings of the Workshop on the ACM Recommender Systems Challenge (Workshop Programme of the Twelfth ACM Conference on Recommender Systems), pp. 1:1–1:6 (2018b)Google Scholar
  24. Kelly, J.P., Bridge, D.: Enhancing the diversity of conversational collaborative recommendations: a comparison. Artif. Intell. Rev. 25(1–2), 79–95 (2006)Google Scholar
  25. Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., et al. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, New York (2011)CrossRefGoogle Scholar
  26. Kula, M.: Mixture-of-tastes models for representing users with diverse interests. CoRR (2017). arXiv:1711.08379
  27. Liang, S., Ren, Z., De Rijke, M.: Personalized search result diversification via structured learning. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 751–760 (2014)Google Scholar
  28. McNee, S.M, Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of the CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 1097–1101 (2006)Google Scholar
  29. Pilászy, I., Zibriczky, D., Tikk, D.: Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 71–78. ACM (2010)Google Scholar
  30. Puthiya Parambath, S.A, Usunier, N., Grandvalet, Y.: A coverage-based approach to recommendation diversity on similarity graph. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 15–22 (2016)Google Scholar
  31. Santos, RLT., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, pp. 881–890 (2010)Google Scholar
  32. Shi, Y., Zhao, X., Wang, J., Larson, M., Hanjalic, A.: Adaptive diversification of recommendation results via latent factor portfolio. In: Proceedings of the 35th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 175–184 (2012)Google Scholar
  33. Smyth, B., McClave, P.: Similarity vs. diversity. In: Proceedings of the International Conference on Case-Based Reasoning, pp. 347–361. Springer, New York (2001)Google Scholar
  34. Su, R., Yin, L., Chen, K., Yu, Y.: Set-oriented personalized ranking for diversified top-n recommendation. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 415–418 (2013)Google Scholar
  35. Tsai, C.H., Brusilovsky, P.: Leveraging interfaces to improve recommendation diversity. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 65–70. ACM (2017)Google Scholar
  36. Tsai, C.H., Brusilovsky, P.: Beyond the ranked list: user-driven exploration and diversification of social recommendation. In: 23rd International Conference on Intelligent User Interfaces, pp. 239–250. ACM (2018)Google Scholar
  37. Vallet, D., Castells, P.: Personalized diversification of search results. In: Proceedings of the 35th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 841–850 (2012)Google Scholar
  38. Vargas Sandoval, S.: Novelty and diversity evaluation and enhancement in recommender systems. Ph.D. thesis, Universidad Autónoma de Madrid, Spain (2015)Google Scholar
  39. Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the 5th ACM Conference on Recommender systems, pp. 109–116 (2011)Google Scholar
  40. Vargas, S., Castells, P.: Exploiting the diversity of user preferences for recommendation. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 129–136 (2013)Google Scholar
  41. Vargas, S., Castells, P., Vallet, D.: Intent-oriented diversity in recommender systems. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1211–1212. ACM (2011)Google Scholar
  42. Vargas. S., Castells, P., Vallet, D.: Explicit relevance models in intent-oriented information retrieval diversification. In: Proceedings of the 35th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 75–84. ACM (2012)Google Scholar
  43. Verstrepen, K., Goethals, B.: Top-N recommendation for shared accounts. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 59–66. (2015)Google Scholar
  44. Wasilewski, J., Hurley, N.: Intent-aware diversification using a constrained PLSA. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 39–42 (2016)Google Scholar
  45. Wasilewski, J., Hurley, N.: Personalised diversification using intent-aware portfolio. In: Adjunct Publication of the 25th ACM Conference on User Modeling, Adaptation and Personalization, pp. 71–76 (2017)Google Scholar
  46. Willemsen, M.C., Graus, M.P., Knijnenburg, B.P.: Understanding the role of latent feature diversification on choice difficulty and satisfaction. User Model. User Adapted Interact. 26(4), 347–389 (2016)CrossRefGoogle Scholar
  47. Zhai, CX., Cohen, WW., Lafferty, J.: Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: Proceedings of the 26th ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 10–17 (2003)Google Scholar
  48. Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2nd ACM Conference on Recommender Systems, pp. 123–130 (2008)Google Scholar
  49. Zhang, M., Hurley, N.: Novel item recommendation by user profile partitioning. In: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology-Volume 01. IEEE Computer Society, pp. 508–515 (2009)Google Scholar
  50. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32 (2005)Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Insight Centre for Data Analytics, School of Computer Science and Information TechnologyUniversity College CorkCorkIreland

Personalised recommendations