Entropy-based Ranking Approach for Enhancing Diversity in Tag-based Community Recommendation

Morteza Rashidi, Ab. Razak Che Hussin, Mehrbakhsh Nilashi

Abstract


Accuracy is the dominant performance evaluation in recommender systems. However, user satisfaction in recommendation includes other factors such diversity and novelty. Some solutions for improving diversity in recommendation, use re-ranking as a post-process on recommendation list achieved from accuracy-aware algorithms. In this work, we propose a method to involve entropy of communities as a diversity factor into the predicted weights from HOSVD method, helping to improve diversity in recommendation list without re-ranking. Experiments on Last.FM dataset, for the case of community recommendation with multi-mode data including users and tags for each community, proves the benefit on introducing diversity factor into the accuracy-based recommendation solutions to improve diversity.


Keywords


Diversity, Recommender Systems, Entropy, Community Recommendation, HOSVD, Multi-Mode data

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References


Adomavicius, G., & Kwon, Y. (2012). Improving Aggregate Recommendation Diversity using Ranking-Based Techniques. IEEE Transactions on Knowledge and Data Engineering, 24(5), 896–911.

Auray, N., & Nationale, E. (2007). Folksonomy: the New Way to Serendipity. Available at SSRN 1009113, 65(65), 1–23.

Cao, Y., Diarmuid, Z., Daniele, Ó. S., Tamas, Q., Zhang, Y. C., Séaghdha, D. Ó., … Jambor, T. (2012). Auralist: introducing serendipity into music recommendation. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 13–22).

Castells, P., Vargas, S., & Wang, J. (2011). Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In International Workshop on Diversity in Document Retrieval (DDR 2011) at the 33rd European Conference on Information Retrieval (ECIR 2011).

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

Herlocker, J. L., Konstan, J. a., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.

Milicevic, A. K., Nanopoulos, A., & Ivanovic, M. (2010). Social tagging in recommender systems: A survey of the state-of-the-art and possible extensions. Artificial Intelligence Review, 33(3), 187–209.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014a). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879-3900.

Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014b). Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 60, 82-101.

Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015b). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications.

Nilashi, M., Ibrahim, O. B., Ithnin, N., & Zakaria, R. (2014c). A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 1-35.

Nilashi, M., Jannach, D., bin Ibrahim, O., & Ithnin, N. (2015a). Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 293, 235-250.

Oestreicher-singer, G., & Sundararajan, A. (2010). Recommendation networks and the long tail of electronic commerce. Available at SSRN 1324064, 36(1), 65–83.

Patil, G. P., Taillie, C., & G. P. Patil and Taillie C. (1982). Diversity as a concept and its measurement. Journal of the American Statistical Association, 77(379), 548–561.

Rendle, S., Schmidt-thieme, L., & Recommender, P. T. (2010). Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining (pp. 81–90).

Steck, H., Labs, B., Hill, M., Management, H. D., Applications, D., & Bias, P. (2011). Item Popularity and Recommendation Accuracy. Proceedings of the 5th ACM Conference on Recommender Systems - RecSys ’11, 125–132.

Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2008). Tag recommendations based on tensor dimensionality reduction. Proceedings of the 2008 ACM Conference on Recommender Systems - RecSys ’08, 43.

Vargas, S., & Castells, P. (2011). Rank and relevance in novelty and diversity metrics for recommender systems. Proceedings of the Fifth ACM Conference on Recommender Systems - RecSys ’11, 109.

Ziegler, C.-N., McNee, S. M., Konstan, J. a., & Lausen, G. (2005). Improving recommendation lists through topic diversification. Proceedings of the 14th International Conference on World Wide Web - WWW ’05, 22.


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