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1. TransCrossCF: Transition-based Cross-Domain Collaborative Filtering

   https://doi.org/10.1109/ICMLA51294.2020.00059  

   Doan, Thanh-Nam; Sahebi, Shaghayegh (December 2020, 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   The success of cross-domain recommender systems in capturing user interests across multiple domains has recently brought much attention to them. These recommender systems aim to improve the quality of suggestions and defy the cold-start problem by transferring information from one (or more) source domain(s) to a target domain. However, most cross-domain recommenders ignore the sequential information in user history. They only rely on an aggregate or snapshot of user feedback in the past. Most importantly, they do not explicitly model how users transition from one domain to another domain as users continue to interact with different item domains. In this paper, we argue that between-domain transitions in user sequences are useful in improving recommendation quality, dealing with the cold-start problem, and revealing interesting aspects of how user interests transform from one domain to another. We propose TransCrossCF, transition-based cross-domain collaborative filtering, that can capture both within and between domain transitions of user feedback sequences while understanding the relationship between different item types in different domains. Specifically, we model each purchase of a user as a transition from his/her previous item to the next one, under the effect of item domains and user preferences. Our intensive experiments demonstrate that TransCrossCF outperforms the state-of-the-art methods in recommendation task on three real-world datasets, both in the cold-start and hot-start scenarios. Moreover, according to our context analysis evaluations, the between-domain relations captured by TransCrossCF are interpretable and intuitive. 

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2. Multi-criteria and Review-Based Overall Rating Prediction

   https://doi.org/10.1007/978-3-030-75765-6_38  

   Ceh-Varela, Edgar; Cao, Huiping; Le, Tuan (January 2021, Pacific-Asia Conference on Knowledge Discovery and Data Mining)

   An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data. 

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3. 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)

   null (Ed.)

   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 measure 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. 

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4. Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering

   Long Chen, Yuan Yao (December 2020, NeuIPS)

   null (Ed.)

   Collaborative filtering has been widely used in recommender systems. Existing work has primarily focused on improving the prediction accuracy mainly via either building refined models or incorporating additional side information, yet has largely ignored the inherent distribution of the input rating data. In this paper, we propose a data debugging framework to identify overly personalized ratings whose existence degrades the performance of a given collaborative filtering model. The key idea of the proposed approach is to search for a small set of ratings whose editing (e.g., modification or deletion) would near-optimally improve the recommendation accuracy of a validation set. Experimental results demonstrate that the proposed approach can significantly improve the recommendation accuracy. Furthermore, we observe that the identified ratings significantly deviate from the average ratings of the corresponding items, and the proposed approach tends to modify them towards the average. This result sheds light on the design of future recommender systems in terms of balancing between the overall accuracy and personalization. 

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5. Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

   https://doi.org/10.1145/3605145  

   Fu, Zhe; Niu, Xi; Maher, Mary Lou (January 2024, ACM Computing Surveys)

   Serendipitous recommendations have emerged as a compelling approach to deliver users with unexpected yet valuable information, contributing to heightened user satisfaction and engagement. This survey presents an investigation of the most recent research in serendipity recommenders, with a specific emphasis on deep learning recommendation models. We categorize these models into three types, distinguishing their integration of the serendipity objective across distinct stages: pre-processing, in-processing, and post-processing. Additionally, we provide a review and summary of the serendipity definition, available ground truth datasets, and evaluation experiments employed in the field. We propose three promising avenues for future exploration: (1) leveraging user reviews to identify and explore serendipity, (2) employing reinforcement learning to construct a model for discerning appropriate timing for serendipitous recommendations, and (3) utilizing cross-domain learning to enhance serendipitous recommendations. With this review, we aim to cultivate a deeper understanding of serendipity in recommender systems and inspire further advancements in this domain. 

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