Recommending with Recommendations

• URL: http://arxiv.org/abs/2112.00979v1
• Date: Thu, 2 Dec 2021 04:30:15 GMT
• Title: Recommending with Recommendations
• Authors: Naveen Durvasula, Franklyn Wang, Scott Duke Kominers
• Abstract summary: Recommendation systems often draw upon sensitive user information in making predictions. We show how to address this deficiency by basing a service's recommendation engine upon recommendations from other existing services. In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space.
• Score: 1.1602089225841632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommendation systems are a key modern application of machine learning, but they have the downside that they often draw upon sensitive user information in making their predictions. We show how to address this deficiency by basing a service's recommendation engine upon recommendations from other existing services, which contain no sensitive information by nature. Specifically, we introduce a contextual multi-armed bandit recommendation framework where the agent has access to recommendations for other services. In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space, and the ideal recommendations for the source and target tasks (which are non-sensitive) are given by unknown linear transformations of the user information. So long as the tasks rely on similar segments of the user information, we can decompose the target recommendation problem into systematic components that can be derived from the source recommendations, and idiosyncratic components that are user-specific and cannot be derived from the source, but have significantly lower dimensionality. We propose an explore-then-refine approach to learning and utilizing this decomposition; then using ideas from perturbation theory and statistical concentration of measure, we prove our algorithm achieves regret comparable to a strong skyline that has full knowledge of the source and target transformations. We also consider a generalization of our algorithm to a model with many simultaneous targets and no source. Our methods obtain superior empirical results on synthetic benchmarks.

Related papers

• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics [0.2537383030441368]
  We propose a Knowledge Graph based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles.
  arXiv  Detail & Related papers  (2024-05-14T09:38:44Z)
• Explainable Recommender with Geometric Information Bottleneck [25.703872435370585]
  We propose to incorporate a geometric prior learnt from user-item interactions into a variational network. Latent factors from an individual user-item pair can be used for both recommendation and explanation generation. Experimental results on three e-commerce datasets show that our model significantly improves the interpretability of a variational recommender.
  arXiv  Detail & Related papers  (2023-05-09T10:38:36Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)
• Two-Stage Neural Contextual Bandits for Personalised News Recommendation [50.3750507789989]
  Existing personalised news recommendation methods focus on exploiting user interests and ignores exploration in recommendation. We build on contextual bandits recommendation strategies which naturally address the exploitation-exploration trade-off. We use deep learning representations for users and news, and generalise the neural upper confidence bound (UCB) policies to generalised additive UCB and bilinear UCB.
  arXiv  Detail & Related papers  (2022-06-26T12:07:56Z)
• PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion [58.870444954499014]
  A common practice is to subsume recommender systems under the decentralized federated learning paradigm. We present a systematic approach to backdooring federated recommender systems for targeted item promotion.
  arXiv  Detail & Related papers  (2021-10-21T06:48:35Z)
• Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks [4.007832851105161]
  We develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm.
  arXiv  Detail & Related papers  (2021-02-18T21:53:32Z)
• Evolving Context-Aware Recommender Systems With Users in Mind [17.817926536931022]
  A context-aware recommender system (CARS) applies sensing and analysis of user context to provide personalized services. We present an approach for selecting low-dimensional subsets of the contextual information and incorporating them explicitly within CARS. Specifically, we present a novel feature-selection algorithm, based on genetic algorithms (GA), that outperforms SOTA dimensional-reduction CARS algorithms.
  arXiv  Detail & Related papers  (2020-07-30T12:03:22Z)
• Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
  We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
  arXiv  Detail & Related papers  (2020-06-03T05:04:38Z)
• Recommendation system using a deep learning and graph analysis approach [1.2183405753834562]
  We propose a novel recommendation method based on Matrix Factorization and graph analysis methods. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and deep embedding method gathers users' latent factors from the user trust graph.
  arXiv  Detail & Related papers  (2020-04-17T08:05:33Z)
• A Survey on Knowledge Graph-Based Recommender Systems [65.50486149662564]
  We conduct a systematical survey of knowledge graph-based recommender systems. We focus on how the papers utilize the knowledge graph for accurate and explainable recommendation. We introduce datasets used in these works.
  arXiv  Detail & Related papers  (2020-02-28T02:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.