Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Recommendation": models, code, and papers

Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations

Feb 14, 2022
Gil I. Shamir, Dong Lin

Real world recommendation systems influence a constantly growing set of domains. With deep networks, that now drive such systems, recommendations have been more relevant to the user's interests and tasks. However, they may not always be reproducible even if produced by the same system for the same user, recommendation sequence, request, or query. This problem received almost no attention in academic publications, but is, in fact, very realistic and critical in real production systems. We consider reproducibility of real large scale deep models, whose predictions determine such recommendations. We demonstrate that the celebrated Rectified Linear Unit (ReLU) activation, used in deep models, can be a major contributor to irreproducibility. We propose the use of smooth activations to improve recommendation reproducibility. We describe a novel family of smooth activations; Smooth ReLU (SmeLU), designed to improve reproducibility with mathematical simplicity, with potentially cheaper implementation. SmeLU is a member of a wider family of smooth activations. While other techniques that improve reproducibility in real systems usually come at accuracy costs, smooth activations not only improve reproducibility, but can even give accuracy gains. We report metrics from real systems in which we were able to productionalize SmeLU with substantial reproducibility gains and better accuracy-reproducibility trade-offs. These include click-through-rate (CTR) prediction systems, content, and application recommendation systems.

  Access Paper or Ask Questions

Exploring The Role of Local and Global Explanations in Recommender Systems

Sep 27, 2021
Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popović, Daniel S. Weld

Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining an individual recommendation, or global, explaining the recommender model in general. Despite their widespread use, there has been little investigation into the relative benefits of these two approaches. Do they provide the same benefits to users, or do they serve different purposes? We conducted a 30-participant exploratory study and a 30-participant controlled user study with a research-paper recommender system to analyze how providing participants local, global, or both explanations influences user understanding of system behavior. Our results provide evidence suggesting that both explanations are more helpful than either alone for explaining how to improve recommendations, yet both appeared less helpful than global alone for efficiency in identifying false positives and negatives. However, we note that the two explanation approaches may be better compared in the context of a higher-stakes or more opaque domain.

  Access Paper or Ask Questions

A Hybrid Bandit Framework for Diversified Recommendation

Dec 24, 2020
Qinxu Ding, Yong Liu, Chunyan Miao, Fei Cheng, Haihong Tang

The interactive recommender systems involve users in the recommendation procedure by receiving timely user feedback to update the recommendation policy. Therefore, they are widely used in real application scenarios. Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set. However, the investigation of users' personalized preferences on the diversity properties of an item set is usually ignored. To overcome this problem, we propose the Linear Modular Dispersion Bandit (LMDB) framework, which is an online learning setting for optimizing a combination of modular functions and dispersion functions. Specifically, LMDB employs modular functions to model the relevance properties of each item, and dispersion functions to describe the diversity properties of an item set. Moreover, we also develop a learning algorithm, called Linear Modular Dispersion Hybrid (LMDH) to solve the LMDB problem and derive a gap-free bound on its n-step regret. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed LMDB framework in balancing the recommendation accuracy and diversity.

* Accepted by AAAI 2021 

  Access Paper or Ask Questions

Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks

Apr 15, 2019
Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty. Keywords: News Recommender Systems, Session-based Recommendation, Artificial Neural Networks, Context-awareness, Hybridization

* 49 pgs 

  Access Paper or Ask Questions

Adversarial Recommendation: Attack of the Learned Fake Users

Sep 21, 2018
Konstantina Christakopoulou, Arindam Banerjee

Can machine learning models for recommendation be easily fooled? While the question has been answered for hand-engineered fake user profiles, it has not been explored for machine learned adversarial attacks. This paper attempts to close this gap. We propose a framework for generating fake user profiles which, when incorporated in the training of a recommendation system, can achieve an adversarial intent, while remaining indistinguishable from real user profiles. We formulate this procedure as a repeated general-sum game between two players: an oblivious recommendation system $R$ and an adversarial fake user generator $A$ with two goals: (G1) the rating distribution of the fake users needs to be close to the real users, and (G2) some objective $f_A$ encoding the attack intent, such as targeting the top-K recommendation quality of $R$ for a subset of users, needs to be optimized. We propose a learning framework to achieve both goals, and offer extensive experiments considering multiple types of attacks highlighting the vulnerability of recommendation systems.

  Access Paper or Ask Questions

The use of Recommender Systems in web technology and an in-depth analysis of Cold State problem

Sep 10, 2020
Denis Selimi, Krenare Pireva Nuci

In the WWW (World Wide Web), dynamic development and spread of data has resulted a tremendous amount of information available on the Internet, yet user is unable to find relevant information in a short span of time. Consequently, a system called recommendation system developed to help users find their infromation with ease through their browsing activities. In other words, recommender systems are tools for interacting with large amount of information that provide personalized view for prioritizing items likely to be of keen for users. They have developed over the years in artificial intelligence techniques that include machine learning and data mining amongst many to mention. Furthermore, the recommendation systems have personalized on an e-commerce, on-line applications such as, Netflix, and As a result, this has inspired many researchers to extend the reach of recommendation systems into new sets of challenges and problem areas that are yet to be truly solved, primarily a problem with the case of making a recommendation to a new user that is called cold-state (i.e. cold-start) user problem where the new user might likely not yield much of information searched. Therfore, the purpose of this paper is to tackle the said cold-start problem with a few effecient methods and challenges, as well as identify and overview the current state of recommendation system as a whole

* 24 pages 

  Access Paper or Ask Questions

AutoLossGen: Automatic Loss Function Generation for Recommender Systems

Apr 27, 2022
Zelong Li, Jianchao Ji, Yingqiang Ge, Yongfeng Zhang

In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a big challenge due to the complexity of the problem. A large fraction of previous work focuses on handcrafted loss functions, which needs significant expertise and human effort. In this paper, inspired by the recent development of automated machine learning, we propose an automatic loss function generation framework, AutoLossGen, which is able to generate loss functions directly constructed from basic mathematical operators without prior knowledge on loss structure. More specifically, we develop a controller model driven by reinforcement learning to generate loss functions, and develop iterative and alternating optimization schedule to update the parameters of both the controller model and the recommender model. One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search. To solve the problem, we further develop a reward filtering mechanism for efficient and effective loss generation. Experimental results show that our framework manages to create tailored loss functions for different recommendation models and datasets, and the generated loss gives better recommendation performance than commonly used baseline losses. Besides, most of the generated losses are transferable, i.e., the loss generated based on one model and dataset also works well for another model or dataset. Source code of the work is available at

* 12 pages, 6 figures. Conference: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22) 

  Access Paper or Ask Questions

Intention Adaptive Graph Neural Network for Category-aware Session-based Recommendation

Dec 31, 2021
Chuan Cui, Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhenwei Dong, Zhihua Wei

Session-based recommendation (SBR) is proposed to recommend items within short sessions given that user profiles are invisible in various scenarios nowadays, such as e-commerce and short video recommendation. There is a common scenario that user specifies a target category of items as a global filter, however previous SBR settings mainly consider the item sequence and overlook the rich target category information in this scenario. Therefore, we define a new task called Category-aware Session-Based Recommendation (CSBR), focusing on the above scenario, in which the user-specified category can be efficiently utilized by the recommendation system. To address the challenges of the proposed task, we develop a novel method called Intention Adaptive Graph Neural Network (IAGNN), which takes advantage of relationship between items and their categories to achieve an accurate recommendation result. Specifically, we construct a category-aware graph with both item and category nodes to represent the complex transition information in the session. An intention-adaptive graph neural network on the category-aware graph is utilized to capture user intention by transferring the historical interaction information to the user-specified category domain. Extensive experiments on three real-world datasets are conducted to show our IAGNN outperforms the state-of-the-art baselines in the new task.

  Access Paper or Ask Questions

Hybrid Model with Time Modeling for Sequential Recommender Systems

Mar 07, 2021
Marlesson R. O. Santana, Anderson Soares

Deep learning based methods have been used successfully in recommender system problems. Approaches using recurrent neural networks, transformers, and attention mechanisms are useful to model users' long- and short-term preferences in sequential interactions. To explore different session-based recommendation solutions, recently organized the WSDM WebTour 2021 Challenge, which aims to benchmark models to recommend the final city in a trip. This study presents our approach to this challenge. We conducted several experiments to test different state-of-the-art deep learning architectures for recommender systems. Further, we proposed some changes to Neural Attentive Recommendation Machine (NARM), adapted its architecture for the challenge objective, and implemented training approaches that can be used in any session-based model to improve accuracy. Our experimental result shows that the improved NARM outperforms all other state-of-the-art benchmark methods.

* ACM WSDM Workshop on Web Tourism (WSDM Webtour'21), March 12, 2021, Jerusalem, Israel 
* 5 pages, 2 figures, WSDM Workshop on Web Tourism 2021 

  Access Paper or Ask Questions

Sequential Recommendation with Relation-Aware Kernelized Self-Attention

Nov 15, 2019
Mingi Ji, Weonyoung Joo, Kyungwoo Song, Yoon-Yeong Kim, Il-Chul Moon

Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention (RKSA) adopting a self-attention mechanism of the Transformer with augmentation of a probabilistic model. The original self-attention of Transformer is a deterministic measure without relation-awareness. Therefore, we introduce a latent space to the self-attention, and the latent space models the recommendation context from relation as a multivariate skew-normal distribution with a kernelized covariance matrix from co-occurrences, item characteristics, and user information. This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics. We experimented RKSA over the benchmark datasets, and RKSA shows significant improvements compared to the recent baseline models. Also, RKSA were able to produce a latent space model that answers the reasons for recommendation.

* AAAI 2020 
* 8 pages, 5 figures, AAAI 

  Access Paper or Ask Questions