Secure social recommendation framework using secret sharing.
problem Privacy concerns and reluctance to share social data in recommender systems.
method Secret Sharing based Matrix Multiplication (SSMM) protocol for secure data sharing and collaborative recommendation.
result SeSoRec framework improves recommendation performance and is secure.
A new method for recommending groups and activities based on geo-social data.
problem Recommendation of groups and activities based on geo-social data with social constraints.
method Proposes an attentive geo-social group recommendation method with an attention mechanism and a spatial query algorithm.
result Significantly outperforms baseline methods in real-world datasets.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
HGP tackles noisy connections and heterogeneity in social graphs for better social recommendation.
problem Challenges in social graphs, especially noisy connections and heterogeneity, hinder GNN-based social recommendation.
method HGP uses a tripartite graph to reduce complexity, personalized PageRank for node embeddings, and attention mechanism for integration.
result HGP outperforms baselines in AUC and F1-score metrics on a large-scale dataset.
NAS model improves social recommendation accuracy using neural attention.
problem Capturing and weighing friends' preferences in social recommendation systems.
method Proposes a Neural Attention mechanism (NAS) for Social collaborative filtering.
result NAS model outperforms state-of-the-art methods in publicly available datasets.
New bandit algorithms adapt to evolving user interests influenced by social circles.
problem Adapting to evolving user interests in recommendation systems.
method Online recommendation algorithms tailored for social influence, based on LinREL and Thompson Sampling.
result Our adaptations maintain asymptotic regret bounds similar to non-social cases.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
problem Recommender systems learn from user choices but can stall if users blindly follow recommendations.
method The recommender learns user knowledge by observing choices, abstaining from recommending a choice when multiple alternatives produce similar payoffs.
result Learning rate and social welfare improve when the recommender abstains from recommending certain choices.
Develops NFCF to reduce gender bias in social media recommendation systems.
problem Reduces gender bias in collaborative filtering systems on social media data.
method Pre-training and fine-tuning neural collaborative filtering with bias correction techniques.
result Achieves better performance and fairness in gender de-biased recommendations.
The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. A social trust and preference segmentation-based matrix factorization (SPMF)…
A deep learning strategy improves recommendation accuracy by leveraging trust and distrust relationships.
problem Data scarcity and cold-start problem in recommender systems.
method Social deep pairwise learning with a ranking loss function and social negative sampling.
result The proposed model achieves an 11.49% improvement over state-of-the-art methods.
We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks. Most real-world social networks are multi-graphs, where different kinds of relationships (e.g. chat, friendship) are possible between a pair of users. Existing network embedding techniques do not leve…
Online voting is an emerging feature in social networks, in which users can express their attitudes toward various issues and show their unique interest. Online voting imposes new challenges on recommendation, because the propagation of votings heavily depends on the structure of social networks as well as the content …
Adversarial framework enforces fairness constraints on graph embeddings.
problem Fairness constraints in graph embeddings, especially age and gender.
method Adversarial framework for compositional fairness constraints.
result Framework allows for flexible combinations of fairness constraints.
The increasing role of recommender systems in many aspects of society makes it essential to consider how such systems may impact social good. Various modifications to recommendation algorithms have been proposed to improve their performance for specific socially relevant measures. However, previous proposals are often …
A new algorithm for social network recommendations using side-observations.
problem Designing recommendation algorithms for users influenced by their social network.
method Contextual bandits with side-observations modeled by a social network graph.
result The proposed algorithm achieves asymptotically optimal regret, matching the lower-bound as To∞. Optimizes long-term social welfare in recommender systems by matching users to providers.
problem Realistic recommender systems dynamics affect all agents, not just users.
method Formulated as an optimal constrained matching problem, solved using dynamical system equilibrium selection.
result Ensures maximal social welfare with diverse viable providers, improving over myopic matching.
Proposes a model to incentivize exploration in web platforms with payments.
problem Learning and social welfare goals of web platforms with myopic users.
method Contextual bandit model with payments to incentivize exploration.
result Achieves sublinear regret while maximizing cumulative social welfare.
Proposes a model to optimize feedback for content creators on social media.
problem Nurturing content creation on social media platforms.
method Modeling feedback distribution to incentivize creators and optimize newsfeed experience.
result Optimized newsfeed for content creators without compromising user experience.
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users. The problem of group recommendation arises naturally in many real world contexts, and is closely related to the budgeted social cho…
Examines how algorithms affect user autonomy and information choice.
problem Impact of algorithmic recommendations on user autonomy and free choice.
method Double dichotomy analysis of user intentions and actions, prior and posterior information rearrangement.
result Algorithms can expand or limit user cognitive and social horizons.
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe a new recommender algorithm that explicitly models negative user prefe…
This paper considers stochastic bandits with side observations, a model that accounts for both the exploration/exploitation dilemma and relationships between arms. In this setting, after pulling an arm i, the decision maker also observes the rewards for some other actions related to i. We will see that this model is su…
Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty …
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using …
Proposes a deep hybrid model for better recommendation systems.
problem Limited studies on hybrid recommender systems and the need for more advanced approaches.
method Integrates deep learning with ID embeddings and auxiliary features for improved recommendation.
result Improves recommendation results over deep learning models using ID embeddings.
The paper proposes a framework to recommend suitable conferences for authors.
problem Helping authors choose appropriate conferences for their research papers.
method Three approaches using social network and paper content, applying Correspondence Analysis (CA).
result Models show better performance than existing methods.
Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm h…
This paper improves fairness in recommendation systems by learning individual preferences across multiple dimensions.
problem Fairness in recommender systems, especially in areas with social impact.
method Opportunistic multi-aspect re-ranking approach that learns individual preferences and enhances provider fairness.
result Achieves a better trade-off between accuracy and fairness across multiple fairness dimensions.
LoCEC classifies user relationships in large social networks, addressing sparsity issues.
problem Sparse relationship feature and label data in real social platforms.
method Local Community-based Edge Classification (LoCEC) framework with three-phase processing.
result Effective and efficient classification of user relationships in large-scale networks.
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
problem Limited exploration of user feedback on Twitter's Home Timeline.
method Release of a large-scale dataset of 160 million Tweets with engagement information.
result Demonstrates challenges in predicting user engagements and privacy concerns.
Identifies influential users in collaborative filtering systems.
problem Impact of influential users on collaborative filtering systems.
method Formalized influence, identified influential users, analyzed impact across domains.
result Identified and characterized influential users and analyzed their impact.
This work surveys algorithmic recourse, aiming to clarify definitions and solutions.
problem Providing explanations and recommendations to individuals affected by automated decisions.
method Literature review and unified definitions, formulations, and solutions.
result Unified definitions and solutions for algorithmic recourse.
Algorithm improves recommendation subset selection in the presence of biases.
problem Maximizing submodular functions for recommendation in the presence of social biases.
method Algorithm for submodular maximization with fairness constraints.
result Algorithm provably outputs subsets with near-optimal utility and proportional representation.
The paper studies and mitigates bias in recommender systems.
problem Bias in recommender systems caused by user feedback loops.
method Modeling user exposure and proposing debiasing strategies.
result Recommender systems are biased and depend on user exposure.
Research uses machine learning to find central nodes and cliques in YouTube social networks.
problem Identifying central nodes and cliques in YouTube social networks.
method Unsupervised machine learning, Python programming, Bron-Kerbosch algorithm.
result Successfully found central nodes through clique-centrality and degree centrality.
Network-based models predict user preferences for items like movies and research articles.
problem Filtering and delivering personalized advice for users with many available products.
method Network models based on group memberships, using Monte Carlo sampling and Expectation-Maximization methods.
result Network models outperform leading approaches for recommendation.
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existi…
HyperFair integrates fairness in recommender systems using probabilistic soft logic.
problem Ensuring fairness in recommender systems across diverse domains.
method Soft fairness constraints integrated as regularization in a joint inference objective function.
result HyperFair improves fairness of predictions from black-box models and hybrid systems.
Debias recommender systems by accounting for hidden confounders using network information.
problem Debiased recommender systems to reduce bias caused by hidden confounders.
method Leverage network information to disentangle user conformity and item popularity, modeling exposure and ratings while controlling hidden confounders.
result The proposed method effectively debiases recommender systems, improving recommendation accuracy.
New algorithm reduces regret in multi-agent bandits with malicious agents.
problem Collaboration between honest and malicious agents in multi-armed bandits.
method Dynamic reduction of communication with malicious agents, learning who is malicious.
result Algorithm reduces regret even with a single malicious agent, assuming m is small compared to K. Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers' social networks (e…
New findings show AI models can't be validated in complex social systems.
problem AI models in complex social systems can't be validated due to data collection practices.
method Formal impossibility results using the MovieLens benchmark.
result AI models in complex social systems are invalid under current data collection practices.
Matrix completion has a long-time history of usage as the core technique of recommender systems. In particular, 1-bit matrix completion, which considers the prediction as a ``Recommended'' or ``Not Recommended'' question, has proved its significance and validity in the field. However, while customers and products aggre…
Study predicts email triage actions using content-based approach.
problem Predicting user triage actions on incoming emails.
method Formulated as a recommendation problem, uses content-based approach with additional similarity features.
result Proposed framework achieves better performance than state-of-the-art deep recommendation methods.
Study shows how leveraging hierarchical similarity graphs improves matrix completion in recommender systems.
problem Improving matrix completion in recommender systems using hierarchical similarity graphs.
method Characterizes the optimal sample complexity using hierarchical stochastic block models and low-rank rating matrices.
result Exploiting hierarchical structure of social graphs significantly reduces the number of observed entries needed for accurate matrix completion.
With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. He…
In this paper, we consider decentralized sequential decision making in distributed online recommender systems, where items are recommended to users based on their search query as well as their specific background including history of bought items, gender and age, all of which comprise the context information of the use…
CRB tackles rising rewards in combinatorial online learning.
problem Rising rewards in combinatorial online learning.
method CRB framework and CRUCB algorithm.
result Empirical and theoretical validation of CRUCB's effectiveness.