Naoto Ohsaka
ABSTRACT
Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity objective. In this study, we revisit the most popular diversity objective called intra-list distance (ILD), defined as the average pairwise distance between selected items, and a similar but lesser known objective called dispersion, which is the minimum pairwise distance. Owing to their simplicity and flexibility, ILD and dispersion have been used in a plethora of diversified recommendation research. Nevertheless, we do not actually know what kind of items are preferred by them.
We present a critical reexamination of ILD and dispersion from theoretical and experimental perspectives. Our theoretical results reveal that these objectives have potential drawbacks: ILD may select duplicate items that are very close to each other, whereas dispersion may overlook distant item pairs. As a competitor to ILD and dispersion, we design a diversity objective called Gaussian ILD, which can interpolate between ILD and dispersion by tuning the bandwidth parameter. We verify our theoretical results by experimental results using real-world data and confirm the extreme behavior of ILD and dispersion in practice.
- Gediminas Adomavicius and YoungOk Kwon. 2012. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng., Vol. 24, 5 (2012), 896--911.Google Scholar
Digital Library
- Gediminas Adomavicius and YoungOk Kwon. 2014. Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J. Comput., Vol. 26, 2 (2014), 351--369.Google ScholarDigital Library
- Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. 2009. Diversifying Search Results. In WSDM. 5--14.Google Scholar
- Enrique Amigó, Damiano Spina, and Jorge Carrillo-de Albornoz. 2018. An Axiomatic Analysis of Diversity Evaluation Metrics: Introducing the Rank-Biased Utility Metric. In SIGIR. 625--634.Google Scholar
- Arda Antikacioglu, Tanvi Bajpai, and R. Ravi. 2019. A New System-Wide Diversity Measure for Recommendations with Efficient Algorithms. SIAM J. Math. Data Sci., Vol. 1, 4 (2019), 759--779.Google ScholarCross Ref
- Azin Ashkan, Branislav Kveton, Shlomo Berkovsky, and Zheng Wen. 2015. Optimal Greedy Diversity for Recommendation. In IJCAI. 1742--1748.Google Scholar
- Benjamin Birnbaum and Kenneth J. Goldman. 2009. An Improved Analysis for a Greedy Remote-Clique Algorithm Using Factor-Revealing LPs. Algorithmica, Vol. 55, 1 (2009), 42--59.Google ScholarDigital Library
- Rubi Boim, Tova Milo, and Slava Novgorodov. 2011. Diversification and Refinement in Collaborative Filtering Recommender. In CIKM. 739--744.Google Scholar
- Allan Borodin, Hyun Chul Lee, and Yuli Ye. 2012. Max-Sum Diversification, Monotone Submodular Functions and Dynamic Updates. In PODS. 155--166.Google Scholar
- Alexei Borodin and Eric M. Rains. 2005. Eynard-Mehta theorem, Schur process, and their Pfaffian analogs. J. Stat. Phys., Vol. 121, 3--4 (2005), 291--317.Google ScholarCross Ref
- Jaime Carbonell and Jade Goldstein. 1998. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. In SIGIR. 335--336.Google Scholar
- Pablo Castells, Neil J. Hurley, and Saul Vargas. 2015. Novelty and Diversity in Recommender Systems. In Recommender Systems Handbook. Springer, 881--918.Google Scholar
- Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to Recommend Accurate and Diverse Items. In WWW. 183--192.Google Scholar
- Charles L. A. Clarke, Maheedhar Kolla, Gordon V. Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. 2008. Novelty and Diversity in Information Retrieval Evaluation. In SIGIR. 659--666.Google Scholar
- Marina Drosou, H.V. Jagadish, Evaggelia Pitoura, and Julia Stoyanovich. 2017. Diversity in Big Data: A Review. Big Data, Vol. 5, 2 (2017), 73--84.Google ScholarCross Ref
- Marina Drosou and Evaggelia Pitoura. 2010. Search Result Diversification. SIGMOD Rec., Vol. 39, 1 (2010), 41--47.Google ScholarDigital Library
- Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In RecSys. 161--168.Google Scholar
- Erhan Erkut. 1990. The discrete p-dispersion problem. Eur. J. Oper. Res. , Vol. 46, 1 (1990), 48--60.Google ScholarCross Ref
- Erhan Erkut and Susan Neuman. 1989. Analytical models for locating undesirable facilities. Eur. J. Oper. Res., Vol. 40, 3 (1989), 275--291.Google ScholarCross Ref
- Damien Garreau, Wittawat Jitkrittum, and Motonobu Kanagawa. 2019. Large sample analysis of the median heuristic. CoRR, Vol. abs/1707.07269 (2019).Google Scholar
- Sreenivas Gollapudi and Aneesh Sharma. 2009. An Axiomatic Approach for Result Diversification. In WWW. 381--390.Google Scholar
- Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, and Kenji Fukumizu. 2012. Optimal kernel choice for large-scale two-sample tests. In NIPS. 1214--1222.Google Scholar
- GroupLens. 2003. MovieLens 1M Dataset. https://grouplens.org/datasets/movielens/1m/ Retrieved April, 2022 fromGoogle Scholar
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Trans. Interact. Intell. Syst., Vol. 5, 4 (2015), 1--19.Google ScholarDigital Library
- Neil Hurley and Mi Zhang. 2011. Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation. ACM Trans. Internet Techn., Vol. 10, 4 (2011), 14:1--14:30.Google ScholarDigital Library
- Marius Kaminskas and Derek Bridge. 2017. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst., Vol. 7, 1 (2017), 2:1--2:42.Google ScholarDigital Library
- Michael J. Kuby. 1987. Programming Models for Facility Dispersion: The p-Dispersion and Maxisum Dispersion Problems. Geographical Analysis, Vol. 19, 4 (1987), 315--329.Google ScholarCross Ref
- Alex Kulesza and Ben Taskar. 2012. Determinantal Point Processes for Machine Learning. Found. Trends Mach. Learn., Vol. 5, 2--3 (2012), 123--286.Google ScholarCross Ref
- Matevž Kunaver and Tomaž Požrl. 2017. Diversity in recommender systems -- A survey. Knowl. Based Syst., Vol. 123 (2017), 154--162.Google ScholarDigital Library
- Odile Macchi. 1975. The coincidence approach to stochastic point processes. Adv. Appl. Probab., Vol. 7, 1 (1975), 83--122.Google ScholarCross Ref
- Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems. In SIGCHI. 1097--1101.Google ScholarDigital Library
- Jianmo Ni. 2018. Amazon review data. https://nijianmo.github.io/amazon/ Retrieved April, 2022 fromGoogle Scholar
- Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP. 188--197.Google Scholar
- Javier Parapar and Filip Radlinski. 2021. Towards Unified Metrics for Accuracy and Diversity for Recommender Systems. In RecSys. 75--84.Google Scholar
- Jeff M. Phillips and Suresh Venkatasubramanian. 2011. A Gentle Introduction to the Kernel Distance. CoRR, Vol. abs/1103.1625 (2011).Google Scholar
- Lijing Qin and Xiaoyan Zhu. 2013. Promoting Diversity in Recommendation by Entropy Regularizer. In IJCAI. 2698--2704.Google Scholar
- S. S. Ravi, Daniel J. Rosenkrantz, and Giri Kumar Tayi. 1994. Heuristic and Special Case Algorithms for Dispersion Problems. Oper. Res., Vol. 42, 2 (1994), 299--310.Google ScholarDigital Library
- Marco Túlio Ribeiro, Anísio Lacerda, Adriano Veloso, and Nivio Ziviani. 2012. Pareto-efficient hybridization for multi-objective recommender systems. In RecSys. 19--26.Google Scholar
- Marco Túlio Ribeiro, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, An'i sio Lacerda, and Adriano Veloso. 2014. Multiobjective pareto-efficient approaches for recommender systems. ACM Trans. Intell. Syst. Technol., Vol. 5, 4 (2014), 1--20.Google ScholarDigital Library
- Tetsuya Sakai and Ruihua Song. 2011. Evaluating Diversified Search Results Using Per-intent Graded Relevance. In SIGIR. 1043--1052.Google Scholar
- Chaofeng Sha, Xiaowei Wu, and Junyu Niu. 2016. A framework for recommending relevant and diverse items. In IJCAI. 3868--3874.Google Scholar
- Barry Smyth and Paul McClave. 2001. Similarity vs. Diversity. In ICCBR. 347--361.Google Scholar
- Harald Steck. 2019. Embarrassingly Shallow Autoencoders for Sparse Data. In WWW. 3251--3257.Google Scholar
- Ruilong Su, Li'Ang Yin, Kailong Chen, and Yong Yu. 2013. Set-oriented Personalized Ranking for Diversified Top-N Recommendation. In RecSys. 415--418.Google Scholar
- Arie Tamir. 1991. Obnoxious Facility Location on Graphs. SIAM J. Discret. Math., Vol. 4, 4 (1991), 550--567.Google ScholarDigital Library
- Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, and Pablo Castells. 2014. Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems. In RecSys. 209--216.Google Scholar
- Saúl Vargas and Pablo Castells. 2011. Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In RecSys. 109--116.Google Scholar
- Jacek Wasilewski and Neil Hurley. 2016. Incorporating Diversity in a Learning to Rank Recommender System. In FLAIRS. 572--578.Google Scholar
- Qiong Wu, Yong Liu, Chunyan Miao, Yin Zhao, Lu Guan, and Haihong Tang. 2019. Recent Advances in Diversified Recommendation. CoRR, Vol. abs/1905.06589 (2019).Google Scholar
- Cong Yu, Laks Lakshmanan, and Sihem Amer-Yahia. 2009. It Takes Variety to Make a World: Diversification in Recommender Systems. In EDBT. 368--378.Google Scholar
- Eva Zangerle and Christine Bauer. 2022. Evaluating Recommender Systems: Survey and Framework. ACM Comput. Surv., Vol. 55, 8 (2022), 1--38.Google Scholar
- Mi Zhang and Neil Hurley. 2008. Avoiding Monotony: Improving the Diversity of Recommendation Lists. In RecSys. 123--130.Google ScholarDigital Library
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving Recommendation Lists Through Topic Diversification. In WWW. 22--32. ioGoogle Scholar
Index Terms
- A Critical Reexamination of Intra-List Distance and Dispersion
Recommendations
Individual Diversity Preference Aware Neural Collaborative Filtering
AbstractThe diversified recommendation of recommender systems enriches user experiences by diversifying recommendation lists. However, the conventional post-processing strategy, which re-ranking the recommendation lists by diversity ...
Highlights- IDP-NCF is an in-processing diversified recommendation method.
- Two elements of ...
Purpose tendency-aware diversified strategy for effective session-based recommendation
AbstractSession-Based Recommender Systems (SBRSs) process time-aware user–item interactions to capture users’ dynamic preferences. Most of the existing SBRSs mainly strive to improve recommendation accuracy by exploiting ...
Highlights- An end to-end learning model, PTDS-SR, is proposed to improve recommendation accuracy and diversity for session-based recommendation.
Multi-interest Diversification for End-to-end Sequential Recommendation
Sequential recommenders capture dynamic aspects of users’ interests by modeling sequential behavior. Previous studies on sequential recommendations mostly aim to identify users’ main recent interests to optimize the recommendation accuracy; they often ...
Comments