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- surveyOctober 2024JUST ACCEPTED
A Survey on Intent-aware Recommender Systems
Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage ...
- research-articleOctober 2024JUST ACCEPTED
Enhancing Recommendation Diversity by Re-ranking with Large Language Models
Recommender Systems (RS) should provide diverse recommendations, not just relevant ones. Diversity helps handle uncertainty and offers users meaningful choices. The literature proposes various methods to improve diversity, most notably by re-ranking and ...
- research-articleOctober 2024JUST ACCEPTED
Improving Effectiveness by Reducing Overconfidence in Large Catalogue Sequential Recommendation with gBCE loss
A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative ...
- research-articleSeptember 2024JUST ACCEPTED
Dynamic fairness-aware recommendation through multi-agent social choice
- Amanda Aird,
- Paresha Farastu,
- Joshua Sun,
- Elena Stefancová,
- Cassidy All,
- Amy Voida,
- Nicholas Mattei,
- Robin Burke
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of ...
- research-articleSeptember 2024JUST ACCEPTED
Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation with Knowledge Graph
Different from the data sparsity that traditional recommendations suffer from, context-aware recommender systems (CARS) face specific sparsity challenges related to contextual features, i.e., feature sparsity and interaction sparsity. How knowledge graphs ...
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- research-articleSeptember 2024JUST ACCEPTED
GIIE: A Graph-based News Recommendation Model with Intrinsic Interest Enhancement
News recommendation aims to offer potentially interesting news items to a specific user, guided by his historical browsing behaviors. Existing methods failed to effectively address the knowledge sparsity issue that the user may have sparse behaviors and ...
- research-articleSeptember 2024JUST ACCEPTED
An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs
In this paper, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized ...
- research-articleSeptember 2024JUST ACCEPTED
A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation aims to alleviate the sparsity problem while capturing users’ sequential preferences. However, most existing methods learn the user preferences in each domain separately, and then perform knowledge transfer between ...
- research-articleAugust 2024JUST ACCEPTED
A Deep Learning Model for Cross-Domain Serendipity Recommendations
Serendipity means unexpected discoveries that are valuable, with positive outcomes ranging from personal benefits to scientific breakthroughs. This study proposes a cross-domain recommendation model, called SerenCDR, to model serendipity. SerenCDR ...
- research-articleAugust 2024JUST ACCEPTED
Denoising and Augmented Negative Sampling for Collaborative Filtering
Negative sampling plays a crucial role in implicit-feedback-based collaborative filtering, where it leverages massive unlabeled data to generate negative signals for guiding supervised learning. The current state-of-the-art approaches focus on utilizing ...
- research-articleAugust 2024JUST ACCEPTED
Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency
This paper explores the biases inherent in ChatGPT-based recommender systems, focusing on provider fairness (item-side fairness). Through extensive experiments and over a thousand API calls, we investigate the impact of prompt design strategies—including ...
- research-articleAugust 2024
Self-Supervised Bot Play for Transcript-Free Conversational Critiquing with Rationales
ACM Transactions on Recommender Systems (TORS), Volume 3, Issue 1Article No.: 7, Pages 1–20https://doi.org/10.1145/3665502Conversational critiquing in recommender systems offers a way for users to engage in multi-turn conversations to find items they enjoy. For users to trust an agent and give effective feedback, the recommender system must be able to explain its suggestions ...
- research-articleAugust 2024
Personalized Cadence Awareness for Next Basket Recommendation
ACM Transactions on Recommender Systems (TORS), Volume 3, Issue 1Article No.: 6, Pages 1–23https://doi.org/10.1145/3652863This empirical study addresses the problem of Next Basket Repurchase Recommendation (NBRR), an often overlooked aspect of Next Basket Recommendation (NBR). While NBR aims to suggest items for a user’s next basket based on their prior basket history, NBRR ...
- research-articleAugust 2024
RADio* – An Introduction to Measuring Normative Diversity in News Recommendations
ACM Transactions on Recommender Systems (TORS), Volume 3, Issue 1Article No.: 5, Pages 1–29https://doi.org/10.1145/3636465In traditional recommender system literature, diversity is often seen as the opposite of similarity and typically defined as the distance between identified topics, categories, or word models. However, this is not expressive of the social science’s ...
- research-articleAugust 2024
User Cold-Start Learning in Recommender Systems using Monte Carlo Tree Search
ACM Transactions on Recommender Systems (TORS), Volume 3, Issue 1Article No.: 3, Pages 1–23https://doi.org/10.1145/3618002We consider the cold-start task for new users of a recommender system, whereby a new user is asked to rate a few items with the aim of quickly discovering the user’s preferences. This is a combinatorial stochastic learning task, and so it is difficult in ...
- research-articleAugust 2024
Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain
ACM Transactions on Recommender Systems (TORS), Volume 3, Issue 1Article No.: 2, Pages 1–24https://doi.org/10.1145/3606950Providing personalized recommendations for insurance products is particularly challenging due to the intrinsic and distinctive features of the insurance domain. First, unlike more traditional domains like retail, movie and so on, a large amount of user ...
- research-articleAugust 2024
RSS: Effective and Efficient Training for Sequential Recommendation Using Recency Sampling
ACM Transactions on Recommender Systems (TORS), Volume 3, Issue 1Article No.: 1, Pages 1–32https://doi.org/10.1145/3604436Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train. Slow training increases the costs of training, hinders product development timescales, and ...
- research-articleJuly 2024
Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation Vectors
- Christina Göpfert,
- Alex Haig,
- Chih-Wei Hsu,
- Yinlam Chow,
- Ivan Vendrov,
- Tyler Lu,
- Deepak Ramachandran,
- Hubert Pham,
- Mohammad Ghavamzadeh,
- Craig Boutilier
ACM Transactions on Recommender Systems (TORS), Volume 2, Issue 4Article No.: 30, Pages 1–37https://doi.org/10.1145/3658675Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e.g., clicks, item consumption, ratings). They allow users to express intent, ...
- research-articleJuly 2024
Incentive-Aware Recommender Systems in Two-Sided Markets
ACM Transactions on Recommender Systems (TORS), Volume 2, Issue 4Article No.: 32, Pages 1–38https://doi.org/10.1145/3674158Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or “arms”) to users (or “agents”). A key challenge in this domain arises from myopic agents who are naturally incentivized to exploit by choosing ...
- research-articleJuly 2024
Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation
ACM Transactions on Recommender Systems (TORS), Volume 2, Issue 4Article No.: 31, Pages 1–27https://doi.org/10.1145/3673244Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user ...