The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation (BIAS@ECIR22)
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Point-of-Interest (POI) recommender system provide personalized recommendation to users while helping businesses attract customers. Highly data-driven recommendations could be impacted by data biases. Most research papers focus on biases only on one stakeholder ignoring the interplays
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When users interact with the recommendations served to them, they leave behind fine-grained traces of interaction patterns, which can be leveraged to predict how satisfying their experience was. This talk will present various works and personal thoughts on how to measure user engagement. It will discuss the definition and development of metrics of user satisfaction that can be used as proxy of user engagement, and will include cases of good, bad and ugly scenarios. An important message will be to show that, to make personalization transparent, it is important to consider the heterogeneity of both user and content to formalise the notion of satisfaction, and in turn design the appropriate satisfaction metrics to capture these. One way to do this is to consider the following angles: 1) Understanding intents; 2) Optimizing for the right metric; 3) Acting on segmentation; and 4) Thinking about diversity.
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1) The AEIOU project aimed to develop a recommendation service using activity data from Welsh educational institutes to increase awareness of Welsh academic research.
2) Focus groups found that undergraduates valued recommendations from successful peers while postgraduates preferred recommendations from trusted sources like tutors.
3) By reporting on usage data and promoting research through recommendations, the repository could help publicize Welsh research, improve collaboration between institutes, and attract potential students and visitors long-term to support sustainable excellence in Welsh higher education research.
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The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation (BIAS@ECIR22)
- 1. The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation Hossein A. Rahmani University College London United Kingdom h.rahmani@ucl.ac.uk Yashar Deldjoo Polytechnic University of Bari Italy deldjooy@acm.org Ali Tourani University of Guilan Iran tourani@msc.guilan.ac.ir Mohammadmehdi Naghiaei University of Southern California, USA naghiaei@usc.edu The 44th European Conference on Information Retrieval (ECIR) Third International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2022) Bias 2022, Stavanger, Norway
- 2. Motivation ● Point-of-Interest (POI) recommender system provide personalized recommendation to users while helping businesses attract customers ● Highly data-driven recommendations could be impacted by data biases ● Most research papers focus on biases only on one stakeholder ignoring the interplays
- 3. Contributions Studying the interplay between <Personalization>, <unfairness of active users>, <unfairness of popular items> ● RQ1: To what extent are users or groups of users (active vs. inactive) are interested in popular POIs? That is identifying natural data bias toward popularity. ● RQ2: Is there a trade-off among the factors of accuracy, user fairness, and item fairness? ● RQ3: Could we classify algorithms based on their performance on the accuracy, user fairness, and item fairness?
- 4. Datasets Number of Users 7,135 Yelp Gowalla 5,628 Number of POIs 15,575 39,943 Number of Check-ins 299,327 618,621 Sparsity 99.74% 99.65% Liu, Yiding, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. "An experimental evaluation of point-of-interest recommendation in location-based social networks." Proceedings of the VLDB Endowment 10, no. 10 (2017): 1010-1021. For each user, the earliest 70% check-ins used as training data, the most recent 20% check-ins as test data, and the remaining 10% as validation data. Time Feb 2016 Feb. 2009 to Oct. 2010
- 5. To what extent are users or groups of users interested in popular POIs? That is identifying natural data bias toward popularity.
- 6. Consumption Distribution of POIs Gowalla Yelp ● A long-term distribution of the POI checks-ins, where few items (POIs) are consumed (visited) by many users, while few users only see most POIs. ● Three categories of items ○ short-head (50% of POIs) ○ mid-tail (30% of POIs) ○ long-tail (20% of POIs) ● User groups ○ Very inactive, slightly inactive, slightly active, and very active based on their number of check-ins. Liu, Y., Pham, T.A.N., Cong, G., Yuan, Q.: An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment 10 (10), 1010–1021 (2017) (a) (b) (c) (d)
- 7. User Profiles, and Popularity Bias Gowalla Yelp ● Increasing the number of items in a user profile extends the probability of finding popular POIs ● Users with fewer items included in their profiles prefer to attend more popular POIs
- 8. Is there a trade-off among the factors of accuracy, user fairness, and item fairness? Could we classify algorithms based on these metrics?
- 9. Evaluation Metrics ● NDCG ● Generalized Cross-Entropy (GCE) ● Mean Absolute Deviation of ranking performance (MADr) Deldjoo, Y., Anelli, V.W., Zamani, H., Bellogin, A., Di Noia, T.: A flexible framework for evaluating user and item fairness in recommender systems. User Modeling and User-Adapted Interaction pp. 1–55 (2021)
- 10. Recommendation Models Conventional Neural CF Context-aware 01 | NeuMF 02 | VAECF 01 | GeoSoCa 02 | LORE 01 | MostPop 02 | BPR 03 | WMF 04 | PF
- 11. Trade-off on Accuracy, User and Item Fairness Yelp Gowalla
- 12. Popularity Bias in POI Recommendation
- 13. Conclusion 01 | We analyzed the trade-off between accuracy, relevance to user groups, and popular item exposures fairness. 02 | Numerous methods failed to balance the item and user fairness due to the natural biases in data. 03 | VAECF produces the best level of accuracy, user fairness, and item fairness. 04 | Usually, one aspect, item fairness or user fairness, is compromised to keep the accuracy high.
- 14. Open Questions 01 | Extending the experiments on more datasets from different domains and models. 02 | Investigating the different type of bias, such as gender bias, can be a potential direction for future research. 03 | Proposing a single metric that can serve for the evaluation of all these dimensions for a given RecSys model.
- 15. Thank you. Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei https://recsys-lab.github.io/FairPOI/