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1. Exploring author gender in book rating and recommendation

   https://doi.org/10.1007/s11257-020-09284-2  

   Ekstrand, Michael D. ; Kluver, Daniel ( February 2021 , User Modeling and User-Adapted Interaction)

   Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of these patterns reflect important real-world phenomena driving interactions between the various users and items; other patterns may be irrelevant or reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to one dimension of social concern, namely content creator gender. Using publicly available book ratings data, we measuremore »the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms tend to propagate at least some of each user’s tendency to rate or read male or female authors into their resulting recommendations, although they differ in both the strength of this propagation and the variance in the gender balance of the recommendation lists they produce. The data, experimental design, and statistical methods are designed to be reusable for studying potentially discriminatory social dimensions of recommendations in other domains and settings as well.« less
2. Estimating Error and Bias in Offline Evaluation Results

   https://doi.org/10.1145/3343413.3378004  

   Tian, Mucun ; Ekstrand, Michael D. ( March 2020 , Proceedings of the 2020 Conference on Computer-Human Interaction and Information Retrieval)

   Offline evaluation protocols for recommender systems are intended to estimate users' satisfaction with recommendations using static data from prior user interactions. These evaluations allow researchers and production developers to carry out first-pass estimates of the likely performance of a new system and weed out bad ideas before presenting them to users. However, offline evaluations cannot accurately assess novel, relevant recommendations, because the most novel recommendations items that were previously unknown to the user; such items are missing from the historical data, so they cannot be judged as relevant. A breakthrough that reliably produces novel, relevant recommendations would score poorly withmore »current offline evaluation techniques. While the existence of this problem is noted in the literature, its extent is not well-understood. We present a simulation study to estimate the error that such missing data causes in commonly-used evaluation metrics in order to assess its prevalence and impact. We find that missing data in the rating or observation process causes the evaluation protocol to systematically mis-estimate metric values, and in some cases erroneously determine that a popularity-based recommender outperforms even a perfect personalized recommender. Substantial breakthroughs in recommendation quality, therefore, will be difficult to assess with existing offline techniques.« less
3. Sequential Recommendation with User Memory Networks

   https://doi.org/10.1145/3159652.3159668  

   Chen, Xu ; Xu, Hongteng ; Zhang, Yongfeng ; Tang, Jiaxi ; Cao, Yixin ; Qin, Zheng ; Zha, Hongyuan ( January 2018 , Web Search and Data Mining)

   User preferences are usually dynamic in real-world recommender systems, and a user’s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms – including both shallow and deep approaches – usually embed a user’s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user’s historical records and future interests. In this paper, we aim to express, store, and manipulate users’ historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we designmore »a memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation. By leveraging the external memory matrix in MANN, we store and update users’ historical records explicitly, which enhances the expressiveness of the model. We further adapt our framework to both item- and feature-level versions, and design the corresponding memory reading/writing operations according to the nature of personalized recommendation scenarios. Compared with state-of-the-art methods that consider users’ sequential behavior for recommendation, e.g., sequential recommenders with recurrent neural networks (RNN) or Markov chains, our method achieves significantly and consistently better performance on four real-world datasets. Moreover, experimental analyses show that our method is able to extract the intuitive patterns of how users’ future actions are affected by previous behaviors.« less
4. Dynamic Tensor Recommender Systems

   Zhang, Yanqing ; Bi, Xuan ; Tang, Niansheng ; Qu, Annie ( February 2021 , Journal of machine learning research)

   Anandkumar, Animashree (Ed.)

   Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed methodmore »utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying coefficient model for temporal tensor factorization through a polynomial spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the spline coefficient estimator. The proposed method is applied to simulations, IRI marketing data and Last.fm data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting.« less
5. TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction

   Fu, Zhe ; Yu, Li ; Niu, Xi ( April 2022 , ACM transactions on knowledge discovery from data)

   As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into a dynamic and personalized recommendations have become a key issue in travel recommender system. In this paper, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE (Travel Reinforcement Recommendations Based on Location-Aware Context Extraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations basedmore »on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.« less